WO2023150768A2 - Biomarkers for acute myeloid leukemia and uses thereof - Google Patents

Biomarkers for acute myeloid leukemia and uses thereof Download PDF

Info

Publication number
WO2023150768A2
WO2023150768A2 PCT/US2023/062078 US2023062078W WO2023150768A2 WO 2023150768 A2 WO2023150768 A2 WO 2023150768A2 US 2023062078 W US2023062078 W US 2023062078W WO 2023150768 A2 WO2023150768 A2 WO 2023150768A2
Authority
WO
WIPO (PCT)
Prior art keywords
subject
aml
drug
mutation
cells
Prior art date
Application number
PCT/US2023/062078
Other languages
French (fr)
Other versions
WO2023150768A3 (en
Inventor
Daniel BOTTOMLY
Brian Druker
Kara Johnson
Stephen Kurtz
Nicola LONG
Shannon MCWEENEY
Anna Reister SCHULTZ
Cristina TOGNON
Jeffrey TYNER
Original Assignee
Oregon Health & Science University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Oregon Health & Science University filed Critical Oregon Health & Science University
Publication of WO2023150768A2 publication Critical patent/WO2023150768A2/en
Publication of WO2023150768A3 publication Critical patent/WO2023150768A3/en

Links

Classifications

    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G01N33/57426—Specifically defined cancers leukemia
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/156—Polymorphic or mutational markers
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • the present disclosure relates to biomarkers for acute myeloid leukemia (AML) and their use, including to predict outcome for subjects with this cancer and to direct treatment toward better outcomes.
  • AML acute myeloid leukemia
  • Acute myeloid leukemia is an aggressive hematologic malignancy characterized by an aberrant proliferation of immature blast cells in peripheral blood and bone marrow that leads to ineffective production of red blood cells and bone marrow failure.
  • the number of new cases among men and women per year is 4.2 per 100,000 population. In the United States, the incidence is over 20,000 cases per year. The average age at the time of diagnosis is about 65 years.
  • Complex genetic and biological features including chromosomal abnormalities and gene mutations contribute to frequent drug resistance and disease relapse.
  • T reatment for AML includes chemotherapies that have been in use for several decades and hematopoietic stem cell/bone marrow transplant. However, durable remission is only achieved for a small percentage of patients.
  • AML is a heterogeneous disease with thousands of somatically mutated genes and numerous chromosomal abnormalities. Gene mutations include single point mutations, insertions, deletions, and duplications. Chromosomal abnormalities include these gene mutations and cytogenetic events have been shown to have prognostic significance.
  • the therapeutics include small molecules and an antibody drug conjugate targeting CD33 (gemtuzumab) for newly diagnosed or relapsed AML expressing CD33.
  • the current disclosure provides biomarkers for acute myeloid leukemia (AML).
  • AML acute myeloid leukemia
  • the biomarkers can be used to predict clinical outcomes for subjects with this disease and to direct treatment for better outcomes.
  • Embodiments provide for a method for determining the prognosis (or diagnosis) for a subject ⁇ 45 years old having AML, including: obtaining a biological sample derived from the subject; measuring in the biological sample a level of platelet endothelial aggregation receptor 1 (PEAR1 ); comparing the measured level of PEAR1 to a threshold level; and assigning to the subject a poor prognosis when the measured level of PEAR1 is greater than the threshold level.
  • the method does not include measuring in the biological sample a level of pyrin domain (PYD) and caspase activation and recruitment domain (CARD) containing (PYCARD) gene or protein.
  • the threshold level includes 0.329 PEAR1 RNA reads per kilobase of transcript per million total reads (RPKM).
  • the biological sample includes peripheral blood or bone marrow aspirate.
  • the subject has one or more mutations or gene rearrangements in TP53, RUNX1, ASXL1, SRSF2, or GATA2-MECOM.
  • the method further includes performing a stem cell transplant for the subject.
  • the treatment regimen for the subject can be modified based on the prognosis.
  • Embodiments also provide for a method of treating a subject ⁇ 45 years old having AML, including administering a therapeutically effective amount of a drug that reduces or eliminates expression of PEAR1 or includes an antagonist of PEAR1 function.
  • the drug includes a nucleic acid.
  • the drug includes an inhibitor of an integrin subunit.
  • the drug includes eptifibatide.
  • the drug includes an antibody, or binding fragment thereof that binds to PEAR1 .
  • Embodiments also provide for a method for identifying a subject having AML who is responsive or resistant to a drug based on their AML cell differentiation state and/or mutational status. BRIEF DESCRIPTION OF THE DRAWINGS
  • FIG. 1 Influence of cell-type on inhibitor response. Drug response modification was quantified by examining the significance (Y-axis) of the interaction between each mutational event and cell-type (x-axis) for each inhibitor, requiring a minimum of 10 mutations. Interactions with q value at two thresholds (q ⁇ 0.1 ; q ⁇ 0.05) are called out by text and shape and distinguished by a dashed and solid line, respectively. Mutation-driven drug response modification of the relationship between inhibitor AUC and cell-type largely occurs within the more-differentiated celltypes and is driven by FLT3-ITD and NRAS. The conditional relationships between sorafenib, FLT3-ITD, and cell-type scores are shown in Figure S3B of Bottomly et al., 2022.
  • FIGs. 2A-2B Genomic associations of drug family response.
  • FIG. 2A Family-level drug response summaries allow identification of cohort-level responses. Some like the Aurora Kinase family (Aurk) show overall resistance and others like Type XIII RTKs (Eph) or PIKK are overall sensitive.
  • FIG. 2B Mutational status associates with drug family response either in terms of significant (q value ⁇ 0.05; Storey et al., Proc Natl Acad Sci U S A 100, 9440-9445, 2003) sensitivity (left of dashed line) or resistance (right of dashed line). Association tests were performed using Welch’s T-test comparing mutated vs wild type and requiring at least 5 mutations for a given mutational event. Effect size was based off of Glass’s delta using wild type as the reference.
  • FIG. 3 Drug family response is influenced by and conditional on cell-type. Mutations that play a significant drug response modification role can be determined, based on the statistical interaction between cell-type score and mutation, requiring at least 10 mutations per event.
  • the Y-axis indicates the signed -Iog10 (P value), the X-axis indicates cell-type.
  • the interactions are listed by text (q value ⁇ 0.1 and q value ⁇ 0.05; Storey etal., Proc Natl Acad Sci U S A 100, 9440- 9445, 2003) and distinguished by a dashed and solid line, respectively.
  • FIGs. 4A-4C Integrative modeling shows PEAR1 expression is a single, independent predictor of poor prognosis, particularly in younger patients and performs similar to LSC17.
  • PEAR1 expression (y-axis) is significantly higher in Adverse compared to Favorable ELN2017 risk categorization (x-axis) for the patients 60 and over (dots; older + oldest group) but the pattern is less pronounced in those patients ⁇ 60. Significance determined using a using a Welch’s T- test.
  • FIG. 4B PEAR1 expression differentiates survival for young patients equivalently to the LSC17 signature for patients with shorter survival times.
  • FIGs. 5A-5D PEAR1 has prognostic ability in entire cohort. PEAR1 performs similarly to the LSC17 signature when using the entire cohort where specimens were obtained at initial acute leukemia diagnosis with splitting defined using our conditional inference tree methodology for both (FIG. 5A, 5B) or split based on the median values for both (FIG. 5C, 5D) (as described in Ng et al., Nature 540, 433-437, 2016, for LSC17).
  • nucleic acid and/or amino acid sequences described herein are shown using standard letter abbreviations, as defined in 37 C.F.R. ⁇ 1.822. Only one strand of each nucleic acid sequence is shown, but the complementary strand is understood as included in embodiments where it would be appropriate.
  • SEQ ID NO: 1 is the amino acid sequence of Homo sapiens PEAR1 (UniProt ID Q5VY43).
  • SEQ ID NO: 2 is the nucleic acid sequence encoding Homo sapiens PEAR1 (nt 130-3243 of Gen Bank NM_001080471 .3).
  • SEQ ID NO: 3 is the nucleic acid sequence of forward primer FLT3.
  • SEQ ID NO: 4 is the nucleic acid sequence of reverse primer FLT3.
  • SEQ ID NO: 5 is the nucleic acid sequence of forward primer NPM1 .
  • SEQ ID NO: 6 is the nucleic acid sequence of reverse primer NPM1 .
  • AML Acute myeloid leukemia
  • these tumors also exhibit a diversity of cellular phenotypes that can be aligned with differentiation states observed in healthy hematopoiesis. These phenotypes have been captured in diagnostic classification schemes such as the French-American-British (FAB) system (Bennett et al., Br J Haematol 33, 451 -458, 1976) as well as diagnostic subsets from the World Health Organization that are based on tumor cell maturation state (Arber etal., Blood V2.7, 2391 -2405, 2016).
  • FAB French-American-British
  • LSC leukemic stem cells
  • MDS myelodysplastic syndromes
  • the present disclosure describes the combining of ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients.
  • the analysis identifies associations of drug response with AML cell differentiation state and/or mutation status. Modeling of clinical outcome reveals a single gene, PEAR1 , to be among the strongest predictors of patient survival, especially for young patients.
  • a biomarker refers to molecular, biological or physical attributes that characterize a physiological, cellular, or disease state and that can be objectively measured to detect or define disease progression or predict or quantify therapeutic responses.
  • a biomarker includes a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.
  • a biomarker can be any molecular structure produced by a cell or organism.
  • a biomarker can be expressed inside any cell or tissue, accessible on the surface of a tissue or cell, structurally inherent to a cell or tissue such as a structural component, secreted by a cell or tissue, produced by the breakdown of a cell or tissue through processes such as necrosis, apoptosis or the like, or a combination thereof.
  • a biomarker may be any protein, carbohydrate, fat, nucleic acid, catalytic site, or any combination of these such as an enzyme, glycoprotein, cell membrane, virus, cell, organ, organelle, or any uni- or multimolecular structure or any other such structure now known or yet to be disclosed whether alone or in combination.
  • a biomarker can be a receptor expressed on a surface of a cell, such as PEAR1 . Because it is present on the surface of a cell, PEAR1 is particularly useful as a biomarker due to its accessibility. It is also useful for instance for cell sorting and other actions that rely on accessibility of the target protein from the outside of a cell.
  • biomarkers disclosed herein can be used to determine the prognosis for a subject ⁇ 45 years old having AML.
  • biomarkers disclosed herein can be used to modify the treatment regimen for a subject ⁇ 45 years old having AML to improve prognosis.
  • Assays known to one of skill in the art can be used to measure a level of a biomarker.
  • the quantity of one or more biomarkers can be indicated as a value.
  • the value can be expressed numerically and result from assaying a sample, and can be derived, e.g., by measuring level(s) of the biomarker(s) in the sample by an assay performed in a laboratory, by measuring the ratio or ratios of the levels of two or more biomarkers, or from a dataset obtained from a provider such as a laboratory, or from a dataset stored on a server.
  • the value may be qualitative or quantitative.
  • the systems and methods provide a reading or evaluation, e.g., assessment, of whether or not the biomarker is present in the sample being assayed.
  • the systems and methods provide a quantitative detection of whether the biomarker is present in the sample being assayed, i.e., an evaluation or assessment of the actual amount or relative abundance of the biomarker in the sample being assayed.
  • the quantitative detection may be absolute or, if the method is a method of detecting two or more different biomarkers in a sample, relative.
  • the term “quantifying” when used in the context of quantifying a biomarker in a sample can refer to absolute or to relative quantification.
  • Absolute quantification can be accomplished by inclusion of known concentration(s) of one or more control biomarkers and referencing, e.g., normalizing, the detected level of the biomarker with the known control biomarkers ⁇ e.g., through generation of a standard curve).
  • relative quantification can be accomplished by comparison of detected levels or amounts between two or more different biomarkers to provide a relative quantification of each of the two or more biomarkers, e.g., relative to each other.
  • the actual measurement of values of the biomarkers can be determined using any method known in the art.
  • a biomarker is detected by contacting a sample with reagents e.g., antibodies or nucleic acids), generating complexes of reagent and biomarker(s), and detecting the complexes.
  • the reagent can include a probe.
  • a probe is a molecule that binds a target, either directly or indirectly.
  • the target can be a biomarker, a fragment of a biomarker, or any molecule that is to be detected.
  • the probe includes a nucleic acid or a protein.
  • a protein probe can be an antibody.
  • An antibody can be a whole antibody or a binding fragment of an antibody.
  • a probe can be labeled with a detectable label. Examples of detectable labels include fluorescent chromophores, chemiluminescent emitters, dyes, enzymes, enzyme substrates, enzyme cofactors, enzyme inhibitors, enzyme subunits, metal ions, and radioactive isotopes.
  • "Protein" detection includes detection of full-length proteins, mature proteins, pre-proteins, polypeptides, isoforms, mutant forms, post-translationally modified proteins and variants thereof, and can be detected in any suitable manner.
  • Antibodies can be conjugated or immobilized to a solid support suitable for a diagnostic assay (e.g., beads such as protein A or protein G agarose, microspheres, plates, slides or wells formed from materials such as latex or polystyrene) in accordance with known techniques, such as passive binding.
  • Antibodies can be conjugated to detectable labels or groups such as radioisotopes (e.g., 35 S, 125 l, 131 1), enzyme labels (e.g., horseradish peroxidase, alkaline phosphatase), and fluorescent labels (e.g., fluorescein, Alexa, green fluorescent protein, rhodamine) in accordance with known techniques.
  • immunoassays include immunoblotting, immunoprecipitation, immunofluorescence, chemiluminescence, electro-chemiluminescence (ECL), and/or enzyme- linked immunosorbent assays (ELISA).
  • the transcript level of a biomarker can be measured by RNA sequencing (RNA-seq).
  • RNA sequencing A protocol for RNA sequencing is described in Tyner et al. (Nature 562, 526-531 , 2018). Briefly, poly(A)+ RNA from an AML sample is chemically fragmented. Double stranded cDNAs are synthesized using random hexamer priming with 3’ ends of the cDNA adenylated and then indexed adaptors are ligated. Library amplification is performed using three-primer PCR using a uracil DNA glycosylase addition for strandedness.
  • RNA from purified CD34+ cells from healthy control bone marrow can serve as control RNA.
  • the control RNA can serve as both a healthy comparator and a quality check on inter-group batch effects.
  • RNA from whole mononuclear bone marrow cells from healthy donors can also be included as control RNA.
  • RNA reads can be mapped and aligned to a reference genome such as GRCh37. All genes with no counts across the samples are excluded. Genes with duplicate gene symbols and those where the counts are ⁇ 10 for 90% or more of the samples are additionally removed prior to normalization similar to the approach suggested for weighted gene correlation network analysis (WGNCA) (Langfelder & Horvath (2008) BMC bioinformatics 9:559). Samples for which their median expression is less than 2 standard deviations below the average are removed from the dataset.
  • WGNCA weighted gene correlation network analysis
  • RPKM values include conditional quantile normalization (CQN)-normalized RPKM.
  • CQN conditional quantile normalization
  • TPM transcriptscripts per million
  • FPKM fragments per kilobase of transcript per million fragments mapped
  • CPM counts per million
  • FPKM is analogous to RPKM and is used for normalizing counts for paired-end RNA-seq data in which two reads are sequenced for each DNA fragment. Counts per million mapped reads are counts scaled by the number of sequenced fragments multiplied by one million. Transcripts per million (TPM) is a measurement of the proportion of transcripts in a pool of RNA.
  • Biomarker expression thresholds for patient stratification are based on the cohort utilized for development of a biomarker signature. Optimization of both the threshold and expression reporter platform can be easily done for clinical populations for transition from research to clinical care by a person of skill in the art for Clinical Laboratory Improvement Amendments (CLIA)- certified laboratory validation.
  • CLIA Clinical Laboratory Improvement Amendments
  • sample refers to a biological material isolated from or derived from a subject.
  • derived from refers to a biological sample being obtained from a subject or other source and including any modification to the sample, addition to the sample, or removal from the sample, as long as biomarkers of the present disclosure can be measured from the sample using the systems and methods of the present disclosure.
  • the biological sample can contain any biological material suitable for detecting a mRNA, polypeptide or other marker of a physiologic or pathologic process in a subject, and can include fluid, tissue, cellular and/or non- cellular material obtained from the individual.
  • a biological sample can include blood, serum, cells, plasma, cerebral spinal fluid, and urine.
  • a biological sample can include serum. Serum from blood is a light yellow, clear liquid that remains after blood has clotted. Serum can be obtained by centrifuging clotted blood. Serum does not include an anticoagulant.
  • a biological sample can include plasma. Plasma is a light yellow, clear liquid that remains when blood clotting is prevented and can be obtained by centrifuging whole blood containing an anti-coagulant.
  • a biological sample may include cells.
  • samples used in the methods of the present disclosure include mononuclear cells isolated from peripheral blood (PBMCs) or from bone marrow aspirates.
  • Mononuclear cells can be isolated by any technique known in the art, including density centrifugation (e.g., with Ficoll-Paque). Density gradient centrifugation separates cells by cell density.
  • PBMC can be isolated by leukapheresis.
  • a leukapheresis machine is an automated device that takes whole blood from a donor and separates out the target PBMC fraction using high-speed centrifugation while returning the remaining portion of the blood, including plasma, red blood cells, and granulocytes, back to the donor.
  • PEAR1 also known as JEDI or MEGF12
  • JEDI JEDI
  • MEGF12 membrane proteins that signal during platelet aggregation
  • Tyrosine phosphorylation occurring through thrombin- or collagen-induced platelet aggregation was shown to be blocked with eptifibatide, an integrin subunit alpha 2b/beta 3 antagonist (Nanda et al., J Biol Chem 280, 24680-24689, 2005).
  • Methylation of the PEAR1 locus has also been examined and methylation state has been shown to correlate with megakaryopoiesis and platelet function (Izzi et al., Clin Epigenetics 11 , 151 , 2019; Izzi et al., Int J Mol Sci 19, 2018; Izzi et al., Blood 128, 1003-1012, 2016).
  • PEAR1 expression is observed on endothelial cells and genetic variation and function of PEAR1 has also been shown to impact on endothelial cell biology (Fisch et al., PLoS One 10, e0138795, 2015; Vandenbriele et al., Cardiovasc Res 108, 124-138, 2015; Zhan et al., Microvasc Res 128, 103941 , 2020).
  • PEAR1 serves as a receptor to trigger clathrin-dependent engulfment of apoptotic neurons that are generated during the development of peripheral ganglia (Sullivan et al., Mol Biol Cell 25, 1925-1936, 2014; Wu et al., Nat Neurosci 12, 1534-1541 , 2009).
  • the signaling underlying this phagocytic process has been studied in mammalian and D. melanogaster models and has been shown to involve SYK, SRC- family kinases, and MAPK8 (JNK; JUN N-terminal kinase) and/or their D. melanogaster homologs (Hilu-Dadia et al., Glia 66, 1520-1532, 2018; Scheib et al., J Neurosci 32, 13022-13031 , 2012).
  • PEAR1 expression is reported to be highest in HSC with decreasing expression as cells differentiate, with the exception that megakaryocyte-erythroid progenitor cells exhibit elevated PEAR1 expression. Forced expression of PEAR1 in bone marrow cells or in fibroblast stromal cells was shown to reduce clonogenic myeloid colony formation (Krivtsov etal., J Cell Biochem 101 , 767-784, 2007).
  • PEAR1 amino acid sequence includes SEQ ID NO: 1 (UniProt ID Q5VY43).
  • PEAR1 is encoded by a sequence including SEQ ID NO: 2 (nt I SO- 3243 of Gen Bank reference sequence NM 001080471 .3).
  • Levels of PEAR1 can be detected at the transcript level or at the protein level using methods known in the art.
  • a level of PEAR1 transcript, expressed as RPKM as described herein, can be measured.
  • antibodies to detect human PEAR1 include: a rabbit polyclonal antibody PA5-21057 (ThermoFisher), goat polyclonal AF4527 (Novus Biologicals), and mouse monoclonal MAB4527 (Novus Biologicals).
  • PEAR1 ELISAs are available commercially (ELH-PEAR1 -2, Ray Biotech).
  • AML is a collection of neoplasms with heterogeneous pathophysiology, genetics and prognosis.
  • Chromosomal abnormalities include chromosome translocations, monosomy, extra copies of chromosomes (e.g., trisomy), and deletion of part or all of a chromosome.
  • chromosomal abnormalities include: deletions of part or all of chromosomes 5 or 7 (—5/— 7 AML); trisomy 8; the long arm of chromosome 11 (1 1q); balanced translocations between chromosomes 15 and 17 (t(15;17)); chromosomes 8 and 21 (t(8;21 )); others such as (q22;q22), (q31 ;q22), and t(9;1 1 ); and inversions such as inv(16).
  • AML patients are presently classified into groups or subsets of AML with markedly contrasting prognosis.
  • the genetic translocations inv( 16), t(8;21 ) and t(15;17) characterize AML with a relatively favorable prognosis
  • the cytogenetically bad-risk leukemias include patients with abnormalities involving 11 q23, loss of 5(q) or 7(q), t(6;9) and t(9;22).
  • a number of genes are observed to be mutated in AML and are associated with good or bad outcome for the disease.
  • the most common molecular abnormality in AML is the internal tandem duplication (ITD) in the fms-like tyrosine kinase-3 gene (FLT3), a hematopoietic growth factor receptor.
  • FLT3 ITD mutations confer a bad prognosis to AML patients.
  • Point mutations and deletion of the tyrosine kinase domain are also seen in AML patients.
  • Transcription factor TP53 functions in cell cycle arrest for DNA mismatch repair, base excision repair, and nucleotide excision repair. Mutations in TP53 are prevalent in relapsed/refractory AML and in older patients with a much lower complete remission. Residual TP53 mutation is associated with resistance to chemotherapy. [0053] In embodiments, AML patients with biallelic mutations in the transcription factor CEBPA have been associated with good outcome. Biallelic mutations in CEBPA (biCEBPA) involve both mutations in N-terminal and C-terminal domains on separate alleles.
  • NPM1 is associated with a higher complete remission, improved overall survival, and a lower cumulative incidence of relapse.
  • NPM1 can be a marker for minimal residual disease (MRD).
  • RUNX1 is a transcription factor that functions in hematopoiesis and is important for defining a hematopoietic stem cell. Chromosomal translocation t(8;21 ) results in a RUNX1 - RUNX1 T 1 fusion protein that promotes cell cycle progression. In embodiments, RUNX1 mutations are associated with inferior outcomes.
  • IDH1 or IDH2 result in neomorphic enzymatic function and production of an oncometabolite, 2-hydroxyglutarate, which can lead to DNA hypermethylation, aberrant gene expression, cell proliferation, and abnormal differentiation.
  • IDH1/2 mutations with NPM1 mutations are associated with improved outcome.
  • DNMT3A is a methyltransferase that is frequently mutated in AML patients.
  • the enzyme functions in de novo methylation of CpG dinucleotides.
  • Two-thirds of AML cases have mutations at R882 in exon 23 of DNMT3A which affects protein function.
  • KMT2A is a lysine-specific methyltransferase. Partial tandem duplications (PTD) in this gene along with other gene mutations or cytogenetic aberrations are associated with adverse outcomes.
  • PTD Partial tandem duplications
  • ASXL1 is an epigenetic regulator and frequently mutated in AML.
  • the presence of both ASXL1 and RUNX1 mutations is related to poor prognosis in AML patients.
  • a mutational status of a subject with AML can be determined by methods known in the art.
  • Mutational status of a subject with AML includes any chromosomal abnormality (translocations, inversions, extra copies of chromosomes, absence of a member of a pair of chromosomes, deletion of a part or all of a chromosome) and gene mutations (single nucleotide polymorphisms, insertions, deletions, internal tandem duplication, partial tandem duplication).
  • the chromosomal abnormalities and/or gene mutations are associated with genes known to be mutated in AML as described herein.
  • Methods to assess mutational status of a subject with AML include whole genome sequencing, whole exome sequencing, gene panels for hematologic malignancies, fluorescence in situ hybridization (FISH), PCR and electrophoresis, and RNA sequencing (RNA-seq).
  • Wholegenome sequencing (WGS) involves creation of an in vitro library from a patient sample by fragmentation of genomic DNA and ligation of adaptors, and then sequencing the genomic fragments. After alignment with a reference genome, the sequence is analyzed for variants.
  • Whole exome sequencing (WES) focuses on sequencing the regions of the genome that encode proteins.
  • Sequencing can use one of many next generation sequencing (NGS) platforms and chemistries available including Illumina, Ion Torrent, and BGI/MGI (Beijing Genomics Institute/MGI Tech Co. Ltd.). Sequencing that requires longer reads can also use a third generation sequencing (3GS) technology such as ones from PacBio and Oxford Nanopore.
  • NGS next generation sequencing
  • 3GS third generation sequencing
  • a non-tumor, germline control sample from a subject with AML such as a skin biopsy, can serve as a control to identify tumor-specific somatic mutations in the AML tumor.
  • Targeted gene panels use NGS to assess the mutational status of multiple genomic regions of interest simultaneously.
  • the targeted panels include specific regions of the genome that are associated with a disease or phenotype of interest, for example, AML.
  • Gene panels can assess point mutations, insertions and deletions, copy number variants (CNV), and translocations.
  • the gene panels can be custom-designed or pre-designed. For example, a 76- gene panel for hematologic malignancies is available from OHSU (GeneTrails) for use in assessing samples from AML patients. Other gene panels for hematologic malignancies are available from Sequenom, Foundation Medicine (UTSW), Genoptix, and Illumina.
  • FISH fluorescent dye-labeled nucleic acid (e.g., DNA) probe is hybridized to a full set of chromosomes from a genome, which are fixed to a glass microscope slide. FISH reveals the location of the labeled probe in the genome.
  • Locus specific probes include a gene of interest (or fragment thereof) that allows visualization of which chromosome the gene is located on or how many copies of a gene exist within a particular genome.
  • Centromeric repeat probes include repetitive sequences found at a chromosome centromere and can be used to assess whether an individual has the correct number of chromosomes.
  • Whole chromosome probes include collections of smaller probes, each of which binds to a different sequence along the length of a given chromosome, resulting in a full-color map of the chromosome known as a spectral karyotype.
  • Whole chromosome probes are useful for examining chromosomal abnormalities, for example, translocations.
  • RNA-seq can help determine single nucleotide polymorphisms and gene fusion products.
  • PCR and electrophoresis can be used to assess gene mutations on a gene-by-gene basis or on a limited number of genes.
  • FLT3 inhibitors can be administered to treat a subject with AML.
  • FLT3 inhibitors include midostaurin, sunitinib, gilteritinib, lestaurtinib (CEP-701), crenolanib, quizartinib, and sorafenib.
  • IDH1 and/or IDH2 inhibitors can be administered to treat a subject with AML.
  • IDH1 inhibitors include ivosidenib (AG-120) and olutasidenib.
  • IDH2 inhibitors include enasidenib (AG-221 /CC-90007) and AGI-6780.
  • romidepsin alone or combined with other chemotherapy drugs can potentially cure or prevent resistance to chemotherapy caused by residual p53 mutation.
  • Vismodegib is a drug that functions as an antagonist of the smoothened receptor in the Hedgehog signaling pathway. It is approved for treatment of basal cell carcinoma.
  • CHIR-99021 IUPAC name: 6-((2-((4-(2,4-Dichlorophenyl)-5- (4-methyl-1 H-imidazol-2-yl)pyrimidin-2-yl)amino)ethyl)amino)nicotinonitrile
  • GSK-3 kinase inhibitor is a GSK-3 kinase inhibitor.
  • Entospletinib is an inhibitor of spleen tyrosine kinase (Syk) in development for various types of cancer.
  • Lapatinib is a synthetic, orally-active quinazoline that reversibly blocks phosphorylation of the epidermal growth factor receptor (EGFR), ErbB2, and the Erk-1 and-2 and AKT kinases.
  • Lapatinib also inhibits cyclin D protein levels in human tumor cell lines and xenografts.
  • Glesatinib MGCD-265 is an orally bioavailable inhibitor of receptor tyrosine kinases, such as MET, VGFR1 -3, Tie, and Ron.
  • Cabozantinib is a medication used to treat medullary thyroid cancer, renal cell carcinoma, and hepatocellular carcinoma. It inhibits the tyrosine kinases c-Met, VEGFR2, AXL, and RET.
  • Barasertib (AZD1152-HQPA) is a selective inhibitor of Aurora B kinase.
  • Canertinib (CI-1033) is an irreversible tyrosine kinase inhibitor effective against EGFR, HER-2, and ErbB-4.
  • NVP-ADW742 is an orally active, selective inhibitor of insulin-like growth factor-1 receptor (IGF-1 R) and also inhibits c-kit kinase.
  • MLN8054 is a selective inhibitor of Aurora A kinase.
  • HSC-like, progenitor-like, granulocyte macrophage progenitor (GMP)-like, promonocyte-like, monocyte-like, and conventional dendritic cell (cDC)-like van Galen etal., Cell 176:1265-1281 , 2019.
  • GMP granulocyte macrophage progenitor
  • cDC dendritic cell
  • determining an AML cell differentiation state can include measuring expression of genes in a biological sample. Particular gene signatures were associated with the six malignant cell types (van Galen et al., Cell 176:1265-1281 , 2019).
  • an AML cell differentiation state is HSC-like when at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least sixteen, or at least seventeen, or at least eighteen, or at least nineteen, or at least twenty, or at least twenty-one, or at least twenty-two, or at least twenty-three, or at least twenty-four, or at least twenty-five, or at least twenty-six, or at least twenty-seven, or at least twenty-eight, or at least twenty-nine, or all of the following genes are expressed in a biological sample derived from an AML patient: NPTX2, H1 F0, EMP1 , MEIS1 , CALCRL, TPSD1 , TPT1 , CRHBP, CLNK, TSC22D1 ,
  • an AML cell differentiation state is progenitor-like, when at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least sixteen, or at least seventeen, or at least eighteen, or at least nineteen, or at least twenty, or at least twenty-one, or at least twenty-two, or at least twenty- three, or at least twenty-four, or at least twenty-five, or at least twenty-six, or at least twenty-seven, or at least twenty-eight, or at least twenty-nine, or all of the following genes are expressed in a biological sample derived from an AML patient: CDK6, HSP90AB1 , SPINK2, EEF1 B2, PCNP, TAPT1 -AS1 , HINT1 , LRRC75A-AS1
  • an AML cell differentiation state is granulocyte macrophage progenitor (GMP)-like, when at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least sixteen, or at least seventeen, or at least eighteen, or at least nineteen, or at least twenty, or at least twenty-one, or at least twenty-two, or at least twenty-three, or at least twenty-four, or at least twenty-five, or at least twenty-six, or at least twenty-seven, or at least twenty-eight, or at least twenty-nine, or all of the following genes are expressed in a biological sample derived from an AML patient: PRTN3, MPO, CALR, CLEC5A, ELANE, POU4F1 , TRH, TSPOAP1 ,
  • an AML cell differentiation state is promonocyte-like, when at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least sixteen, or at least seventeen, or at least eighteen, or at least nineteen, or at least twenty, or at least twenty-one, or at least twenty-two, or at least twenty- three, or at least twenty-four, or at least twenty-five, or at least twenty-six, or at least twenty-seven, or at least twenty-eight, or at least twenty-nine, or all of the following genes are expressed in a biological sample derived from an AML patient: DEFB1 , RNASE2, MS4A3, SERPINB10, SESN3, ZFR, MRPL33, CTSG, SLC44A1 , SLPI, FUT4,
  • an AML cell differentiation state is monocyte-like, when at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least sixteen, or at least seventeen, or at least eighteen, or at least nineteen, or at least twenty, or at least twenty-one, or at least twenty-two, or at least twenty- three, or at least twenty-four, or at least twenty-five, or at least twenty-six, or at least twenty-seven, or at least twenty-eight, or at least twenty-nine, or all of the following genes are expressed in a biological sample derived from an AML patient: FCN1 , S100A12, MAFB, VCAN, S100A9, PLBD1 , SERPINA1 , BCL2A1 , THBS1 , PSAP
  • an AML cell differentiation state is conventional dendritic cell (cDC)-like at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least sixteen, or at least seventeen, or at least eighteen, or at least nineteen, or at least twenty, or at least twenty-one, or at least twenty-two, or at least twenty-three, or at least twenty-four, or at least twenty-five, or at least twenty-six, or at least twenty-seven, or at least twenty-eight, or at least twenty-nine, or all of the following genes are expressed in a biological sample derived from an AML patient: MRC1 , HLA-DRB5, CST3, SAMHD1 , NAPSB, FCER1A, HLA-DRB1 , JAML, P
  • Methods are known to one of skill in the art for measuring gene expression, including RNA- seq, microarrays, and qRT-PCR.
  • determining an AML cell differentiation state can include analyzing AML cells microscopically and classifying an AML cell differentiation state by the French-American- British (FAB) system.
  • FAB subtypes M0-M2 had high HSC-like cell type scores, while FAB subtypes M4-M5 correlate with the monocyte-like cell type (see FIG. 3B).
  • determining an AML cell differentiation state can include detecting cell marker expression or absence by flow cytometry.
  • HSC-like markers include: Lin-, CD11 b-, CD14-, CD15-, CD36-, CD38-, CD45RA-, CD64-, CD34+, CD49f+, and CD90+.
  • progenitor-like markers include: Lin-, CD38-, CD45RA-, CD49f-, CD90-, CD34+, CD117+, CD123+, and CD302+.
  • GMP-like markers include: Lin-, CD15+, CD16+, CD24+, CD34+, CD38+, and CD45RA+.
  • promonocyte-like markers include: CD34-, CD1 1 b+, CD13+, CD14+, CD15+, CD36+, CD64+, and CD117+.
  • monocyte-like markers include: Lin-, CD16-, CX3CR1 -, CD1 1 b+, CD14++, CD62L+, CD64+, CD68+, CD1 15+, CD163+, CD369+, CCR2 high, HLA-DR+, and VCAN+.
  • conventional dendritic cell-like markers include: BDCA-1 +, CD8+, CD8alpha+, CD11 b+, CD11c+, CD103+, CD205+, CD206+, CD369+, HLA-DR+, and MHC Class II+.
  • the present disclosure provides for a method for determining the prognosis for a subject ⁇ 45 years old having AML, including: obtaining a biological sample derived from the subject; measuring in the biological sample a level of PEAR1 ; comparing the measured level of PEAR1 to a threshold level; and assigning to the subject a poor prognosis when the measured level of PEAR1 is greater than the threshold level.
  • the method does not include measuring in the biological sample a level of pyrin domain (PYD) and caspase activation and recruitment domain (CARD) containing (PYCARD) gene or protein.
  • the threshold level includes 0.329 PEAR1 RNA reads per kilobase of transcript per million total reads (RPKM).
  • the biological sample includes peripheral blood or bone marrow aspirate.
  • the subject has one or more mutations or gene rearrangements in TP53, RUNX1, ASXL 1, SRSF2, or GATA2-MECOM.
  • the method further includes performing a stem cell transplant for the subject.
  • Poor prognosis for a subject ⁇ 45 years old having AML when the measured level of PEAR1 is greater than the threshold level can include: a decreased chance of overall survival, a decreased chance of relapse-free survival, a decreased chance of metastasis-free survival, a decrease in the time of survival (e.g., less than 10 years, less than 5 years, or less than one year), presence of a malignant tumor, an increase in the severity of disease, a decrease in response to therapy, an increase in tumor recurrence, an increase in metastasis, or the like.
  • a poor prognosis includes a 75% survival probability or less at 500 days, or a 70% survival probability or less at 1000 days, or a 63% survival probability or less at 1500 days, or a 63% survival probability or less at 2000 days, or a 50% survival probability or less at 2500 days, or a 45% survival probability or less at 2500 days, from time of diagnosis or first treatment or remission.
  • poor prognosis for a subject ⁇ 45 years old having AML when the measured level of PEAR1 is greater than the threshold level can take into consideration other prognostic factors described herein.
  • methods are also enabled for using PEAR1 to diagnose a subject, for instance to diagnose a subject ⁇ 45 years of age as having AML.
  • a subject who is thusly diagnosed with AML can further be selected for treatment, and/or treated for AML.
  • Methods of treatment are known in the art, and are described herein.
  • the present disclosure provides for a method of treating a subject ⁇ 45 years old having AML, including administering a therapeutically effective amount of a drug that reduces or eliminates expression of PEAR1 or includes an antagonist of PEAR1 function.
  • the drug includes a nucleic acid.
  • the nucleic acid can include an antisense RNA, a small interfering RNA, or a microRNA that disrupts expression of the PEAR1 gene.
  • the drug includes an inhibitor of an integrin subunit.
  • the drug inhibits allbp3 integrin.
  • the drug includes eptifibatide.
  • Eptifibatide is an antiplatelet drug that reversibly binds and inhibits glycoprotein llb/llla receptor of platelets. Eptifibatide is used to reduce ischemic cardiac events.
  • the drug includes an antibody, or binding fragment thereof that binds to PEAR1 .
  • the present disclosure provides for a method of modifying a treatment regimen of a subject ⁇ 45 years old having AML, including: obtaining a biological sample derived from the subject; measuring in the biological sample a level of PEAR1 ; comparing the measured level of PEAR1 to a threshold level; and modifying the treatment regimen when the measured level of PEAR1 is greater than the threshold level.
  • the method does not include measuring in the biological sample a level of pyrin domain (PYD) and caspase activation and recruitment domain (CARD) containing (PYCARD) gene or protein.
  • the threshold level includes 0.329 PEAR1 RNA reads per kilobase of transcript per million total reads (RPKM).
  • the biological sample includes peripheral blood or bone marrow aspirate.
  • the modifying the treatment regimen includes enrolling the subject in a clinical trial testing a drug for treatment of AML. In embodiments, In embodiments, the modifying the treatment regimen includes stopping chemotherapy and providing a stem cell transplant for the subject. In embodiments, the modifying the treatment regimen includes changing from one chemotherapy drug or a combination of chemotherapy drugs to another chemotherapy drug or another combination of chemotherapy drugs. In embodiments, the subject has one or more mutations or gene rearrangements in TP53, RUNX1, ASXL1, SRSF2, or GATA2- MECOM.
  • the present disclosure provides for a method for identifying a subject having AML who is responsive to a drug, including: obtaining a biological sample derived from a subject having AML; determining a differentiation state of cells in the biological sample; determining mutational status of the subject; and identifying the subject as responsive to a drug.
  • the subject is responsive to vismodegib (GDC-0449) when the differentiation state of cells is hematopoietic stem cell (HSC)-like and the subject has an MLLT3-KMT2A gene fusion.
  • the subject is responsive to sorafenib when the differentiation state of cells is progenitor-like and the subject has an FL73-ITD mutation.
  • the subject is responsive to CHIR-99021 when the differentiation state of cells is promonocyte-like and the subject has at least one mutation in KRAS.
  • the subject is responsive to entospletinib (GS-9973), lapatinib, sunitinib, MGCD-265, or a combination thereof, when the differentiation state of cells is monocytelike and the subject has at least one mutation in NRAS.
  • the subject is responsive to AGI-6780 when the differentiation state of cells is monocyte-like and the subject has at least one mutation in U2AF1.
  • the present disclosure provides for a method for identifying a subject having AML who is resistant to a drug, including: obtaining a biological sample derived from a subject having AML; determining a differentiation state of cells in the biological sample; determining mutational status of the subject; and identifying the subject as resistant to a drug.
  • the subject is responsive to cabozantinib, MGCD-265, or sunitinib when the differentiation state of cells is promonocyte-like and the subject has an FL73-ITD mutation.
  • the subject is responsive to MGCD-265, sunitinib, AZD1152-HQPA (AZD281 1 ), canertinib, sorafenib, cabozantinib, NVP-ADW742, or a combination thereof, when the differentiation state of cells is monocyte-like and the subject has an FL73-ITD mutation.
  • the subject is responsive to MLN8054 when the differentiation state of cells is monocyte-like and the subject has at least one mutation in IDH2.
  • the subject is responsive to CHIR-99021 when the differentiation state of cells is monocyte-like and the subject has at least one mutation in SRSF2.
  • the present disclosure provides for a method for identifying a subject having acute myeloid leukemia (AML) who is responsive to a drug, including: obtaining a biological sample derived from a subject having AML; determining mutational status of the subject; and identifying that the subject is responsive to a drug.
  • AML acute myeloid leukemia
  • the subject is responsive to a cyclin-dependent kinase (CDK) inhibitor when the subject has at least one mutation in NRAS.
  • the subject is responsive to a cyclin-dependent kinase (CDK) inhibitor when the subject has at least one mutation in KRAS.
  • the CDK inhibitor comprises JNJ-7706621 , R547, roscovitine (CYC-202), flavopiridol, palbociclib, AST-487, AT7519, BMS-345541 , linifanib (ABT-869), or SNS-032 (BMS- 387032).
  • the subject is responsive to an AGC kinase inhibitor when the subject has a CBFB-MYH11 gene fusion.
  • an AGC kinase inhibitor comprises H-89, Go6976, LY-333531 , PP242, Midostaurin, BMS-345541 , AKT Inhibitor IV, GSK690693, MK-2206, or AKT Inhibitor X.
  • the subject is responsive to a phosphatidylinositol kinase inhibitor when the subject has at least one mutation in RUNX1. In embodiments, the subject is responsive to a phosphatidylinositol kinase inhibitor when the subject has at least one mutation in STAG2. In embodiments, the subject is responsive to a phosphatidylinositol kinase inhibitor when the subject has at least one mutation in TP53. In embodiments, a phosphatidylinositol kinase inhibitor comprises PI-103, TG100-115, BEZ235, GDC-0941 , LY294002, Idelalisib, or PP242.
  • a SRC kinase inhibitor comprises bosutinib (SKI- 606), dasatinib, ibrutinib (PCI-32765), PD173955, ponatinib (AP24534), PP2, vandetanib (ZD6474), saracatinib (AZD0530), PLX-4720, KW-2449.
  • a JAK kinase inhibitor comprises JAK Inhibitor I, JNJ-7706621 , ruxolitinib (INCB018424), tofacitinib (CP-690550), CYT387, PP242, TG101348, midostaurin, pelitinib (EKB-569), or BMS-345541.
  • the subject is responsive to a phosphatidylinositol-3 kinase-related kinase inhibitor when the subject has at least one mutation in TP53.
  • a phosphatidylinositol-3 kinase-related kinase inhibitor comprises BEZ235, PI-103, PP242, Rapamycin, INK-128, or KU-55933.
  • Biomarkers and drug/mutational status/AML cell differentiation state associations described herein can be used to treat subjects (humans, veterinary animals (dogs, cats, reptiles, birds, etc.), livestock (horses, cattle, goats, pigs, chickens, etc.), and research animals (monkeys, rats, mice, fish, etc.)). Treating subjects includes providing therapeutically effective amounts. Therapeutically effective amounts include those that provide effective amounts, prophylactic treatments, and/or therapeutic treatments.
  • an “effective amount” is the amount of a composition necessary to result in a desired physiological change in a subject. Effective amounts are often administered for research purposes. Representative effective amounts disclosed herein can reduce symptoms associated with AML, improve overall survival, and/or promote complete remission.
  • a "prophylactic treatment” includes a treatment administered to a subject who does not display signs or symptoms of a disease or nutritional deficiency or displays only early signs or symptoms of AML.
  • a "therapeutic treatment” includes a treatment administered to a subject who has AML.
  • AML Acute myeloid leukemia refers to a rapidly progressing cancer of the blood and bone marrow that affects a group of white blood cells (WBC) called myeloid cells.
  • WBC white blood cells
  • AML can also be referred to as acute myelogenous leukemia, acute myeloblastic leukemia, acute granulocytic leukemia and acute nonlymphocytic leukemia.
  • a hematological malignancy refers to a cancer that affects the blood or bone marrow.
  • Symptoms of AML include: fever; weakness and fatigue; loss of weight and appetite; aches and pains in the bones or joints; tiny red spots in the skin; easy bruising and bleeding; frequent minor infections; and poor healing of minor cuts.
  • AML can be diagnosed using a karyotype analysis, which assesses the size, shape, and number of chromosomes in a biological sample.
  • the biological sample includes a blood sample or bone marrow sample.
  • Cytogenetic and gene mutations can also be detected by molecular methods, including whole genome sequencing, whole exome sequencing, gene panels for hematologic malignancies, PCR, and RNA sequencing.
  • AML can be characterized by morphology of the cancerous cells.
  • AML can be classified by the type of normal, immature white blood cell it most closely resembles. The most common subtype is myeloid leukemia, where the cancer is in cells that normally produce neutrophils, phagocytes that make up 40% to 70% of WBC in humans.
  • Another AML subtype includes monoblastic or monocytic leukemia. In monocytic leukemia, the cells look like monocytes, the largest WBC that can differentiate into macrophages and dendritic cells.
  • leukemia cells can be a mixture of myeloblastic and monocytic cells.
  • AML appear to originate from erythroid cells (that produce red blood cells) or platelets (megakaryocytic).
  • Acute promyelocytic leukemia is a unique subtype of AML where the cancer cell stops maturing when the cell is at a stage called the promyelocyte or progranulocyte stage.
  • APL is associated with a translocation between chromosomes 15 and 17 [t(15;17)].
  • a classification system for AML from the World Health Organization includes these major groups of AML: AML with recurrent genetic abnormalities; AML with multilineage dysplasia (defined as the presence of 50% or more dysplastic cells in at least 2 cell lines); AML related to previous therapy (e.g., chemotherapy or radiation); AML, not otherwise categorized (NOS) (these include cases similar to the FAB classification, acute basophilic leukemia, and acute panmyelosis with fibrosis); myeloid sarcoma (also known as granulocytic sarcoma or chroroma); myeloid proliferations related to Down Syndrome; and undifferentiated and biphenotypic acute leukemias (leukemias with both lymphocytic and myeloid features).
  • AML with recurrent genetic abnormalities AML with multilineage dysplasia (defined as the presence of 50% or more dysplastic cells in at least 2 cell lines); AML related to previous therapy (e.g., chemotherapy or radiation); AML, not otherwise categorized (
  • AML subtypes may also be classified by the French-American-British (FAB) classification system, which is based on morphology and cytochemistry (Bennett et al., Ann Intern Med. 103(4):620-625, 1985).
  • FAB French-American-British
  • the subtypes include: MO, myeloblastic without differentiation; M1 , myeloblastic with little or no maturation; M2, myeloblastic with maturation; M3, promyelocytic (e.g., APL); M4, myelomonocytic; M4eo, myelomonocytic with eosinophils; M5a, monocytic without differentiation (monoblastic); M5b, monocytic with differentiation; M6, erythroid leukemia; and M7, megakaryoblastic leukemia.
  • the MO to M5 subtypes start in immature forms of WBC.
  • the M6 subtype starts in immature forms of red blood cells.
  • the M7 subtype starts in immature forms of cells that make platelets.
  • AML can be classified by the cytogenetic, or chromosome, changes found in leukemia cells.
  • particular chromosomal changes are closely matched with the morphology of the AML cells.
  • Chromosomal changes are commonly grouped according to the likelihood that treatment will work against the subtype of AML.
  • Types of cytogenetic changes found in AML include: a translocation, where a chromosome breaks off and reattaches to another chromosome; an inversion, where a single chromosome undergoes breakage and rearrangement within itself; extra copies of a chromosome; and a deletion of a part or all of a chromosome.
  • Chemotherapy is the primary treatment for AML. Chemotherapy is the use of drugs to destroy cancer cells. In embodiments, chemotherapy inhibits or reduces cancer cells’ ability to grow and divide. Systemic chemotherapy is delivered through the bloodstream to reach cancer cells throughout the body. Chemotherapy can be administered: by an intravenous (IV) tube placed into a vein using a needle; by injection into the cerebral spinal fluid (CSF); in a pill or capsule that is swallowed (orally); and/or by a subcutaneous injection. In embodiments, when chemotherapy is administered into a larger vein, a central venous catheter or port can be placed in the body.
  • IV intravenous
  • CSF cerebral spinal fluid
  • a central venous catheter or port can be placed in the body.
  • a chemotherapy regimen, or schedule usually includes a specific number of cycles given over a set period of time.
  • a patient receives one drug at a time.
  • a patient receives combinations of 2 or more different drugs given at the same time.
  • drugs are used to treat AML, which are discussed herein.
  • Chemotherapy for AML can be divided into 3 phases: induction, post-remission, and consolidation.
  • Induction therapy is the first period of treatment after a subject’s diagnosis.
  • the goal of induction therapy is a complete remission (CR).
  • a subject has a CR when: blood counts have returned to normal; leukemia cannot be found in a bone marrow sample when examined under the microscope; and/or there are no longer any signs and symptoms of AML.
  • induction therapy can include two rounds of chemotherapy.
  • a combination of cytarabine (Cytosar-U) and an anthracycline drug e.g., daunorubicin (Cerubidine) or idarubicin (Idamycin)
  • an anthracycline drug e.g., daunorubicin (Cerubidine) or idarubicin (Idamycin)
  • cytarabine is administered over 4 to 7 days and daunorubicin or idarubicin is administered for 3 days.
  • a 7+3’ regimen is followed in induction therapy, where cytarabine is infused continuously for 7 days and then an anthracycline is administered for 3 days.
  • hydroxyurea (Droxia, Hydrea) is administered to a subject with AML during induction therapy.
  • hydroxyurea helps lower WBC counts.
  • WBC counts can be reduced temporarily by leukapheresis.
  • leukapheresis the patient’s blood is passed through a special machine that removes WBC (including leukemia cells) and returns the rest of the blood to the patient. Intravenous lines or a single catheter is used for the leukapheresis.
  • hypomethylating agents decitabine (Dacogen) and/or azacytidine (Vidaza), and/or low dose cytarabine is administered to older adults.
  • other chemotherapy drugs that are used include: cladribine (2-CdA), fludarabine, mitoxantrone, etoposide (VP-16), 6-thioguanine (6-TG), corticosteroid drugs (e.g., prednisone or dexamethasone), methotrexate (MTX), and 6-mercaptopurine (6-MP).
  • cladribine (2-CdA) fludarabine
  • mitoxantrone etoposide
  • VP-16 6-thioguanine
  • corticosteroid drugs e.g., prednisone or dexamethasone
  • methotrexate MTX
  • 6-mercaptopurine 6-MP
  • Post-remission therapy includes administering a variety of different drugs to destroy AML cells that remain but cannot be detected by medical tests. AML will almost certainly recur if no further treatment is given after a CR. In embodiments, a subject undergoes a bone marrow/stem cell transplantation as part of post-remission therapy.
  • Consolidation therapy is continued treatment provided to keep AML from recurring.
  • Consolidation therapy includes chemotherapy and stem cell transplantation.
  • younger adults in remission are administered 2 to 4 rounds of high- or intermediate-dose cytarabine or other intensive chemotherapy at monthly intervals.
  • the following chemotherapy drugs can be used in consolidation therapy: amsacrine, high dose cytarabine, etoposide, daunorubicin, fludarabine, and idarubicin.
  • each chemotherapy drug is administered to a subject with AML at a therapeutically effective dose.
  • cytarabine is administered at 100 mg/m 2 /d by continuous IV infusion on days 1-7 for induction therapy.
  • cytarabine is administered at 100 mg/m 2 IV every 12 hours on days 1-7 for induction therapy.
  • cytarabine is administered at 3 g/m 2 IV over 1 -3 hours every 12 hours for four doses for relapsed ALL.
  • daunorubicin is administered at 30-45 mg/m 2 /d IV for 3 days in combination therapy for remission induction therapy. In embodiments, total cumulative dose for daunorubicin is 550 mg/m 2 . In embodiments, a liposomal preparation of daunorubicin is administered at 40 mg/m 2 IV every 2 weeks.
  • cladribine is administered at 0.09 mg/kg/d (4 mg/m 2 /d) by continuous IV infusion for 7 consecutive days.
  • fludarabine is administered at 25 mg/m 2 /d IV over 30 min for 5 days. In embodiments, the administration is repeated every 28 days.
  • hydroxyurea is administered intermittently at 80 mg/kg PO every third day. In embodiments, hydroxyurea is administered continuously at 20-30 mg/kg PO daily.
  • idarubicin is administered at 12 mg/m 2 /d IV for 3 days every 3 weeks in combination therapy.
  • methotrexate is administered at a low dose of 2.5-5.0 mg PO daily, or 5-25 mg/m 2 PO, IM, IV twice weekly, or 50 mg/m 2 IV every 2-3 weeks. In embodiments, methotrexate is administered at a high dose of 1-12 g/m 2 IV with leucovorin rescue every 1-3 weeks. In embodiments, methotrexate is administered intrathecally at 5-10 mg/m 2 (up to 15 mg) every 3-7 days.
  • mitoxantrone is administered at 12 mg/m 2 /d IV for 3 days, in combination with cytarabine for remission induction therapy.
  • chemotherapy also targets dividing cells in healthy tissue
  • side effects of chemotherapy include losing hair, developing mouth sores, nausea, and vomiting.
  • subjects with AML undergoing chemotherapy receive drugs to alleviate side effects of chemotherapy.
  • Chemotherapy lowers the body’s ability to fight infection, which can lead to increased bruising, bleeding, and/or fatigue.
  • subjects with AML undergoing chemotherapy receive antibiotics to prevent and treat infections and/or receive transfusions of red blood cells and platelets.
  • the targeted drug gemtuzumab ozogamicin Mylotarg
  • Mylotarg can be added to chemotherapy treatment.
  • AML can be treated with a stem cell transplant or a bone marrow transplant.
  • the medical procedure involves destroying cancer cells in the marrow, blood, and other parts of the body using high doses of chemotherapy and/or radiation therapy and then introducing replacement blood stem cells, called hematopoietic stem cells (HSC), to create healthy bone marrow.
  • HSC are blood- forming cells found both in the bloodstream and in the bone marrow.
  • the procedure is a stem cell transplant when stem cells in the blood are transplanted.
  • the procedure is a bone marrow transplant when bone marrow tissue is transplanted.
  • the stem cell transplantation can be allogeneic (stem cells originating from a donor) or autologous (stem cells originating from the subject with AML). In embodiments, allogeneic stem cell transplants are used for AML.
  • AML can be treated with targeted therapy.
  • Targeted therapy targets the leukemia’s specific genes, proteins, or the tissue environment that contributes to the growth and survival of the leukemia.
  • targeted therapy blocks the growth and spread of leukemia cells while limiting damage to healthy cells.
  • a subject who has relapsed or refractory AML with an IDH2 mutation is administered enasidenib (Idhifa).
  • a subject who has relapsed or refractory AML with a FLT3 gene mutation is administered gilteritinib (Xospata).
  • a subject who has relapsed or refractory AML with an IDH1 gene mutation is administered ivosidenib (Tibsovo).
  • a subject who has AML with a FLT3 gene mutation is administered midostaurin (Rydapt).
  • Refractory AML occurs when leukemia is still present after initial treatment.
  • a subject with the APL subtype of AML is administered a combination of an all-trans retinoic acid (ATRA) and arsenic trioxide (Trisenox).
  • ATRA is administered orally.
  • a subject with the APL subtype who receives a combination of ATRA and arsenic trioxide are very likely to have a CR.
  • a subject with the APL subtype of AML can receive chemotherapy containing regimens with idarubicin, daunorubicin, and/or cytarabine.
  • arsenic trioxide can be used alone during induction therapy.
  • arsenic trioxide can be used in combination with ATRA during post-remission therapy or if APL comes back after treatment.
  • AML can be treated with radiation therapy.
  • Radiation therapy is the use of high-energy x- rays or other particles to destroy cancer cells.
  • radiation therapy includes external-beam radiation therapy, which is radiation given from a machine outside the body.
  • a radiation therapy regimen, or schedule includes a specific number of treatments given over a set period of time.
  • radiation therapy is used when leukemia cells have spread to the brain or to shrink a myeloid sarcoma. Side effects from radiation therapy may include fatigue, mild skin reactions, upset stomach, and loose bowel movements.
  • Palliative or supportive care can be undertaken to relieve a subject’s symptoms and side effects due to treatment of AML.
  • Palliative or supportive care includes supporting the patient with their physical, emotional, and social needs.
  • Palliative treatments can include medication, nutritional changes, relaxation techniques, emotional support, and other therapies.
  • a subject with AML is in remission or having “no evidence of disease” (NED) when the leukemia cannot be detected in the body, there are no symptoms, and/or a patient’s blood counts are normal.
  • a subject with AML is in remission when a bone marrow biopsy shows few bone marrow cells (hypocellular bone marrow) and only a small portion of blasts (making up no more than 5% of the bone marrow).
  • a remission may be temporary or permanent. If the leukemia does return after the original treatment, it is called recurrent or relapsed leukemia.
  • treatment for recurrent or relapsed AML can include the treatments described herein, such as chemotherapy, stem cell transplantation, targeted therapy, and radiation therapy, but they may be used in a different combination or given at a different pace.
  • the treatment for recurrent AML often depends on the length of the initial remission. If the AML comes back after a long remission, the original treatment can work again. If the remission was short, then other drugs can be used, often through a clinical trial. An allogeneic stem cell transplant may be the best option for patients whose leukemia has come back after initial treatment. However, many drugs and other approaches are being researched in clinical trials and these may provide other treatment options.
  • a number of factors are known to contribute to the outlook for a subject with AML or a subject’s risk of AML recurring after treatment, including AML subtype, age, genetic changes, cytogenetic changes, WBC count, response to chemotherapy, presence or absence of minimal residual disease, whether the AML is therapy-related, time of relapse, history of another blood disorder, uncontrolled infection at the time of diagnosis of AML, and spread of AML to the central nervous system.
  • chromosomal (cytogenetic) changes can contribute to prognosis.
  • chromosomal changes are grouped as follows: 1 ) favorable (associated with more successful treatment): translocation or inversion of chromosome 16, a translocation between chromosomes 8 and 21 , and a translocation between chromosomes 15 and 17; 2) intermediate: chromosomal changes associated with a less favorable prognosis include normal chromosomes, where no changes are found and a translocation between chromosomes 9 and 1 1 [t(9;1 1 )]; 3) unfavorable: chromosomal changes that are associated with less successful treatment or with a low chance of curing the AML include extra copies of chromosomes 8 or 13 [for example, trisomy 8 (+8)], deletion of all or part of chromosomes 5 or 7, changes to chromosome 3 at band q26, translocation between chromosomes 6 and 9, translocation between chromosome
  • extra copies of chromosome 8 or trisomy 8 may be classified as intermediate risk or unfavorable.
  • Human chromosomes are numbered from 1 to 22. Human sex chromosomes are called “X” or “Y.” The letters “p” and “q” refer to the “arms” or specific areas of the chromosome.
  • age can contribute to prognosis. In embodiments, younger adults (e.g., ⁇ 60 years of age) have a more favorable prognosis than older adults.
  • chromosomal abnormalities can happen as a person gets older. In embodiments, older people can have other health conditions that make it difficult to cope with side effects of treatments for AML.
  • gene mutations can contribute to prognosis. Testing for molecular changes at diagnosis helps determine a patient’s treatment options. Up to 50% of people with AML have a mutation in the nucleophosmin (NPM1) gene. In embodiments, the NPM1 gene mutation is linked with a more favorable prognosis if there are no other abnormalities. 30% of people with AML have an internal tandem duplication, FLT3-ITD, in the FMS-like tyrosine kinase 3 (FLT3) gene. In embodiments, changes to the CEBPA gene are linked to a more favorable prognosis. In embodiments, FLT3-ITD is linked with a less favorable prognosis.
  • patients with changes in the NPM1 or CEBPA genes have a better long-term outcome, while chemotherapy does not work as well for patients with changes in the FLT3 gene.
  • overexpression of the EPG gene in people with AML points to a less favorable prognosis.
  • WBC count can contribute to prognosis.
  • a WBC count of more than 100,000 at the time of diagnosis is linked with a less favorable prognosis for AML.
  • favorable changes occur more commonly in subjects ⁇ 60 years of age with AML, while unfavorable changes are more common in people > 60 years of age with AML.
  • treatment is successful in the long term for 50% to 60% of subjects younger than 60 with AML that is classified as favorable and for less than 10% of patients ⁇ 60 years of age with AML that is classified as unfavorable. Prognosis in patients older than 60 years of age is significantly worse.
  • response to chemotherapy can contribute to prognosis.
  • People who reach complete remission after induction chemotherapy have a more favorable prognosis than those who have refractory disease that does not respond to treatment.
  • the response to chemotherapy is measured as the time it takes to reach a complete remission, or complete response.
  • the prognosis is more favorable when a complete remission is reached within 4 weeks of starting chemotherapy.
  • the prognosis is less favorable when it takes > 4 weeks to reach complete remission.
  • the prognosis is poorer in subjects with AML who don’t reach a complete remission after chemotherapy.
  • a subject with AML who has minimal residual disease any time after the start of consolidation therapy (the continued treatment given to keep leukemia from coming back) has a higher risk of relapse and a poorer prognosis.
  • MRD refers to the presence of leukemia cells, or blasts, in the bone marrow, that can only be detected by very sensitive tests, such as flow cytometry or polymerase chain reaction (PCR) and not by standard tests such as microscopy.
  • a subject with AML who has early relapse has poorer (i.e. less favorable) prognosis.
  • An early relapse means that the leukemia returns soon after treatment.
  • Recurrent or relapsed AML is cancer that has come back after treatment.
  • a subject who has AML that develops after treatment for another cancer has poorer (i.e. less favorable) prognosis.
  • a subject with AML who has or already had another blood disorder such as a myelodysplastic syndrome (MDS)
  • MDS myelodysplastic syndrome
  • a subject who has a serious, uncontrolled infection at the time of diagnosis of AML has poorer (i.e. less favorable) prognosis.
  • spread of AML to the brain and spinal cord i.e. the central nervous system, or CNS
  • CNS central nervous system
  • Therapeutic treatments can be distinguished from effective amounts based on the presence or absence of a research component to the administration. As will be understood by one of ordinary skill in the art, however, in human clinical trials effective amounts, prophylactic treatments and therapeutic treatments can overlap.
  • therapeutically effective amounts can be initially estimated based on results from in vitro assays and/or animal model studies. Such information can be used to more accurately determine useful doses in subjects of interest.
  • the actual dose amount administered to a particular subject can be determined by the subject, a physician, veterinarian, or researcher taking into account parameters such as physical, physiological and psychological factors including target, body weight, condition, previous or concurrent therapeutic interventions, and/or idiopathy of the subject.
  • Therapeutically effective amounts can be achieved by administering a dose including 0.0001 pg/kg body weight to 10 mg/kg body weight per dose, or 0.0001 pg/kg body weight to 0.001 pg/kg body weight per dose, or 0.001 pg/kg body weight to 0.01 pg/kg body weight per dose, or 0.01 pg/kg body weight to 0.1 pg/kg body weight per dose, or 0.1 pg/kg body weight to 10 pg/kg body weight per dose, or 1 pg/kg body weight to 100 pg/kg body weight per dose, or 100 pg/kg body weight to 500 pg/kg body weight per dose, or 500 pg/kg body weight per dose to 1000 pg/kg body weight per dose, or 1 .0 mg/kg body weight to 10 mg/kg body weight per dose.
  • Therapeutically effective amounts can be achieved by administering single or multiple doses during the course of a treatment regimen (e.g., hourly, every 2 hours, every 3 hours, every 4 hours, every 6 hours, every 9 hours, every 12 hours, every 18 hours, daily, every other day, every 3 days, every 4 days, every 5 days, every 6 days, weekly, every 2 weeks, every 3 weeks, or monthly).
  • a treatment regimen e.g., hourly, every 2 hours, every 3 hours, every 4 hours, every 6 hours, every 9 hours, every 12 hours, every 18 hours, daily, every other day, every 3 days, every 4 days, every 5 days, every 6 days, weekly, every 2 weeks, every 3 weeks, or monthly.
  • One or more drug(s) can be administered simultaneously or within a selected time window, such as within 10 minutes, 1 hour, 3 hour, 10 hour, 15 hour, 24 hour, or 48 hour time windows or when the complementary active agent(s) is within a clinically-relevant therapeutic window.
  • Treatment can include administering a drug by any appropriate route including: orally; parenterally including buccally, sublingually, sublabially, mucosal; by inhalation; intra-arterially; intravenously; intraventricularly; intramuscularly; subcutaneously; transdermally; intraspinally; intraorbitally; intracranially; and intrathecally.
  • AML acute myeloid leukemia
  • AML acute myeloid leukemia
  • a method for identifying a subject having acute myeloid leukemia (AML) who is responsive to a drug including: obtaining a biological sample derived from a subject having AML; determining a differentiation state of cells in the biological sample; determining mutational status of the subject; and identifying the subject as responsive to a drug when (a) the differentiation state of cells is hematopoietic stem cell (HSC)-like and the subject has an MLLT3-KMT2A gene fusion;
  • HSC hematopoietic stem cell
  • the differentiation state of cells is promonocyte-like and the subject has at least one mutation in KRAS; (d) the differentiation state of cells is monocyte-like and the subject has at least one mutation in NRAS; and/or (e) the differentiation state of cells is monocyte-like and the subject has at least one mutation in U2AF1 .
  • a method of diagnosing a subject as having drug responsive acute myeloid leukemia comprising: obtaining a biological sample derived from a subject having AML; determining a differentiation state of cells in the biological sample; determining mutational status of the subject; and identifying the subject as responsive to a drug when (a) the differentiation state of cells is hematopoietic stem cell (HSC)-like and the subject has an MLLT3-KMT2A gene fusion;
  • HSC hematopoietic stem cell
  • the differentiation state of cells is promonocyte-like and the subject has at least one mutation in KRAS; (d) the differentiation state of cells is monocyte-like and the subject has at least one mutation in NRAS; and/or (e) the differentiation state of cells is monocyte-like and the subject has at least one mutation in U2AF1 .
  • a method for identifying a subject having acute myeloid leukemia (AML) who is resistant to a drug including: obtaining a biological sample derived from a subject having AML; determining a differentiation state of cells in the biological sample; determining mutational status of the subject; and identifying the subject as resistant to a drug when (a) the differentiation state of cells is promonocyte-like and the subject has an FLT3-ITD mutation; (b) the differentiation state of cells is monocyte-like and the subject has an FLT3-ITD mutation; (c) the differentiation state of cells is monocyte-like and the subject has at least one mutation in IDH2; and/or (d) the differentiation state of cells is monocyte-like and the subject has at least one mutation in SRSF2.
  • AML acute myeloid leukemia
  • a method of diagnosing a subject as having drug resistant acute myeloid leukemia comprising: obtaining a biological sample derived from a subject having AML; determining a differentiation state of cells in the biological sample; determining mutational status of the subject; and identifying the subject as resistant to a drug when (a) the differentiation state of cells is promonocyte-like and the subject has an FLT3-ITD mutation; (b) the differentiation state of cells is monocyte-like and the subject has an FLT3-ITD mutation; (c) the differentiation state of cells is monocyte-like and the subject has at least one mutation in IDH2; and/or (d) the differentiation state of cells is monocyte-like and the subject has at least one mutation in SRSF2.
  • AML drug resistant acute myeloid leukemia
  • a method for identifying a subject having acute myeloid leukemia (AML) who is responsive to a drug including: obtaining a biological sample derived from a subject having AML; determining mutational status of the subject; and identifying that the subject is responsive to a drug when: (a) the subject has at least one mutation in NRAS, and wherein the drug includes a cyclin-dependent kinase (CDK) inhibitor; (b) the subject has at least one mutation in KRAS, and wherein the drug includes a cyclin-dependent kinase (CDK) inhibitor; (c) the subject has a CBFB- MYH11 gene fusion, and wherein the drug includes a AGC kinase inhibitor; (d) the subject has at least one mutation in RUNX1 , and wherein the drug includes a phosphatidylinositol kinase inhibitor; (e) the subject has at least one mutation in STAG2, and wherein the drug includes a
  • a method for identifying a subject having acute myeloid leukemia (AML) who is responsive to a drug comprising: obtaining a biological sample derived from a subject having AML; determining mutational status of the subject; and identifying that the subject is responsive to a drug when (a) the subject has at least one mutation in NRAS, and wherein the drug comprises a cyclin-dependent kinase (CDK) inhibitor; (b) the subject has at least one mutation in KRAS, and wherein the drug comprises a cyclin-dependent kinase (CDK) inhibitor; (c) the subject has a CBFB-MYH1 1 gene fusion, and wherein the drug comprises a AGC kinase inhibitor; (d) the subject has at least one mutation in RUNX1 , and wherein the drug comprises a phosphatidylinositol kinase inhibitor; (e) the subject has at least one mutation in STAG2, and wherein the drug comprises a
  • AML acute myeloid leukemia
  • Example 1 Integrative analysis of drug response and clinical outcome in acute myeloid leukemia.
  • This Example describes the identification of a single gene, platelet endothelial aggregation receptor 1 (PEAR1 ), that can be used as a biomarker to predict outcome for an AML patient ⁇ 45 years of age. Integrative analysis of data including genomic, transcriptomic, cell differentiation states, and clinical annotations also reveal broad association of drug sensitivity with tumor cell differentiation state, new predictors of drug sensitivity based on mutational status and/or cell-type, and additional associations between drug response, cell-type, and mutational status based on organization of drugs into gene and pathway target families. At least some of these data described in this Example was published as Bottomly et al. (Cancer Cell 40:850-865, August 8, 2022; referred to herein as Bottomly et al., 2022).
  • the complete OHSU Beat AML cohort represents sample collection and characterization over a span of 10 years with integration of ex vivo drug sensitivity testing, curation of clinical annotations, and DNA- and RNA-sequencing to reveal mutational status and gene expression profiles.
  • the data in Tyner etal. (Nature 562, 526-531 , 2018) include the first two waves of patient accrual and sample data from 11 academic medical centers (denoted as “Waves 1 +2”).
  • Waves 3+4 include a total of 293 patient specimens from 279 patients (243 patients unique to Waves 3+4). Also provided were harmonization of these data sets together, for a cumulative cohort of 942 specimens from 805 patients, which reflected a real-world cohort of AML cases, inclusive of de novo, transformed, and therapy-related AML as well as cases at the point of initial diagnosis (70% of cases) and smaller number of cases with relapsed or residual disease. All somatic variant calls, gene expression counts, and drug response data can be explored and visualized through the interactive browser, Vizome (vizome.org).
  • Waves 3+4 can serve as validation cohort to assess prior mutation-inhibitor associations (Tyner et al., Nature 562, 526-531 , 2018) (shown in Figure 1 D of Bottomly et al., 2022).
  • RNA-Seq data were harmonized across the waves to maintain consistency with the Waves 1 +2 results (see Figure 2A of Bottomly et al., 2022), by re-using the parameters and reference distribution of the conditional quantile normalization (CQN; Hansen et al., Biostatistics 13, 204-216, 2012) procedure applied previously.
  • CQN conditional quantile normalization
  • Prior weighted gene co-expression network analysis WGCNA; Zhang & Horvath, Stat Appl Genet Mol Biol 4, Articlel 7, 2005 identified 13 gene expression modules across Waves 1 +2 (Tyner et al., Nature 562, 526-531 , 2018).
  • Part of the WGCNA methodology involved the summarization of each module by its first principal component (PC) score (termed an eigengene; Horvath & Dong, PLoS computational biology 4, e10001 17, 2008).
  • PC principal component
  • the parameters e.g. means, standard deviations and rotations
  • the PC scores of the Waves 3+4 samples for each module were predicted.
  • Examination of the first two PCs of these 13 WGCNA modules revealed near complete overlap in terms of range, distributions, and clustering of the two datasets in every module (see Figure 2B of Bottomly etal., 2022), highlighting the effectiveness of the harmonization strategy.
  • AML Cell Maturation State Deconvoluting AML Cell Maturation State. It has been observed that drug response patterns in AML are sometimes correlated with maturation state of AML tumor cells, with certain drugs exhibiting stronger efficacy against tumors with less differentiated cell state (e.g.
  • BCL2i, CDK4/6i (Kuusanmaki et al., Haematologica 105, 708-720, 2020; Majumder et al., Haematologica, 2019; Pei et al., Cancer Discov O, 536-551 , 2020; Romine et al., Blood Cancer Discov 2, 518-531 , 2021 ; Zhang et al., Nat Cancer 1 , 826-839, 2020)) and others showing better efficacy against tumors with more differentiated state (e.g. BETi, MEKi; (Romine et aL, 2021 ; White, B.S. et al., NPJ Precis Oncol 5, 71 , 2021 )).
  • FIG. S2A of Bottomly et al., 2022 An example of the formation of the Monocyte-like score is shown in Figure S2A of Bottomly et al., 2022. Comparison of the least differentiated HSC-like score or one of the most differentiated Monocyte-like scores against FAB subtypes shows higher overall scores for HSC-like in the less differentiated M0-M2 samples and higher Monocyte-like scores in the myelomonocytic and monocytic M4-M5 groups (as shown in Figure S2B of Bottomly et al., 2022). Differentiation scoring correlates with clinical annotation of tumor maturation state.
  • the third cluster showed some, albeit muted, activity against both HSC-/Progenitor-like and cDC-/Monocyte-like states with the most resistance conferred by GMP- and Promonocyte-like states (see Figure 3A of Bottomly et al., 2022). Influence of cell-type on inhibitor response. Correlating inhibitor AUC with the six cell-type scores allows inhibitors (x-axis) to be divided into three main groups based on the relationship between differentiation and resistance (blue) or sensitivity (red) (as shown in Figure 3A of Bottomly et al., 2022).
  • sorafenib is a potent inhibitor of the FLT3 receptor tyrosine kinase and exhibits significantly greater efficacy in FLT3-ITD mutant samples.
  • inclusion of cell-type scores reveals that sorafenib shows stronger efficacy in cases with a higher Progenitor-like score and the existence of a prominent Monocyte-like signal confers resistance of FLT3-ITD-positive AML cases to sorafenib (see Figure S3B of Bottomly etal., 2022).
  • Drug Family Responses Drug response data were organized into groups of inhibitors based on shared targets or pathways. Drug-target relationship data from the Cancer Targetome (Blucher et al., Trends in Pharmacological Sciences 38, 1085-1099, 2017; Blucher et al., F1000 Research 8, 908, 2019; Choonoo etal., PLoS One 14, e0223639, 2019) and other sources (Davis et al., Nat Biotechnol 29, 1046-1051 , 201 1 ) were aggregated to yield 25 drug families with some overlap of drugs between families based on known polypharmacology. Variable overall response of drug families across all samples tested was observed, with some families (e.g.
  • Phosphatidyl inositol 3' kinase-related kinases PIKK
  • PIKK Phosphatidyl inositol 3' kinase-related kinases
  • kME platelet endothelial aggregation receptor 1
  • Each variable type was grouped on the Y-axis and the displayed association values were derived from the corresponding test statistics (as shown in Figure 6A of Bottomly et al., 2022). These include the Z statistic for categorical (logistic regression) and survival outcomes (Cox Proportional Hazards) and T-statistic for the continuous outcomes (general linear model). Average linkage hierarchical clustering was applied to rows (within groups) and columns (as showing in Figure 6B of Bottomly et al., 2022; top) Of all the 203 genes in module 3, PEAR1 has the highest correlation with the module eigengene (kME).
  • the age groups are young ( ⁇ 45), middle (45-60), older (60-75), oldest (>75).
  • Depiction of the resulting surrogate ctree with variables indicated as ovals with indicated significance for the optimal split (see Figure 6C of Bottomly et al., 2022).
  • Lines indicate values that were split on, for instance, “TRUE” for the ‘young’ variable, which indicates that the subgroup is ⁇ 45 years old.
  • the resulting subgroups of patients, rectangles that denote ‘terminal nodes’, are listed with the subgroup size (denoted as n) and are colored to match corresponding survival curves.
  • PEAR1 expression was compared to the leukemic stem cell 17-gene signature (LSC17) (Ng et al., Nature 540, 433-437, 2016) in this young subset of patients and it was observed that expression of PEAR1 could distinguish outcomes in young patients to a similar degree as the LSC17 signature (FIG. 4B herein).
  • LSC17 leukemic stem cell 17-gene signature
  • the hazard ratios for PEAR1 and LSC17 were assessed in all age groups and in the young subset. Additionally, cases where the specimen obtained was a bone marrow aspirate were further stratified, due to improved splitting of outcomes observed with bone marrow compared with peripheral blood specimens (see Figures S4A-S4D of Bottomly et al., 2022).
  • Prognostic association of PEAR1 is dependent on originating tissue type. Considering the entire cohort of Denovo patient RNASeq samples with survival information and limiting to peripheral blood or leukapheresis samples, a significant split of PEAR1 using a conditional inference tree (ctree) was not found (as shown in Figure S4A of Bottomly et al., 2022). However, using bone- marrow derived samples allowed differentiation of prognostic groups (as shown in Figure S4B of Bottomly et al., 2022).
  • ctree conditional inference tree
  • prognostic groups are again only able to be formed using a ctree approach in the bone-marrow derived samples (as shown in Figures S4C and S4D of Bottomly et al., 2022); significance was determined using the Log Rank test.
  • the hazard ratios revealed similar performance of PEAR1 with LSC17 (FIG. 4C herein), noting predictive range is influenced by subset and split type (see Methods).
  • a recent study examined the AML Cancer Genome Atlas dataset (Cancer Genome Atlas Research, N Engl J Med 368, 2059-2074, 2013) to identify an immune prognostic signature for poor prognostic subsets of AML.
  • AML Cancer Genome Atlas dataset
  • PEAR1 an immune prognostic gene expression model
  • PYCARD another gene
  • This immune prognostic model showed correlation with T-cell scores deconvoluted from TCGA transcriptomic data as well as expression of certain immune checkpoint molecules. It was also shown to correlate with poor clinical outcome in several AML datasets; however, a role for PEAR1 in shaping leukemic cell biology or cell-type was not considered.
  • Platelet levels have been shown to decline with age (Biino et al., PLoS One 8, e54289, 2013), and a leukocyte pool with enhanced platelet aggregation potential - possibly facilitated by increased PEAR1 downstream of somatic mutational events such as ASXL 1 - could represent a selective advantage within an aging hematopoietic microenvironment.
  • PEAR1 As noted herein, PEAR1 phosphorylation can be blocked with an inhibitor of the integrin subunits that facilitate PEAR1 activation downstream of platelet aggregation (eptifibatide). PEAR1 also appears to be an active signaling molecule in this context, suggesting that smallmolecule approaches could mitigate PEAR1 signaling as a potential therapeutic avenue. Antibodies to PEAR1 have also been developed, so a large-molecule approach to PEAR1 targeting may also be feasible.
  • this Example exemplifies the utility of integrative analysis that incorporates functional testing of large cohorts of primary patient specimens.
  • most observations have been validated in two independently collected datasets, and the harmonized dataset as well as cutting-edge analytical methods have been leveraged to extract many new findings.
  • These include broad association of drug sensitivity with tumor cell differentiation state, new predictors of drug sensitivity based on mutational status, sometimes conditional on cell-type, and additional associations between drug response, cell-type, and mutational status based on organization of drugs into gene and pathway target families. Given the strong associations that were seen between cell-type score and drug response, the distribution of these cell phenotypes should be assessed when evaluating clinical differences in drug sensitivity.
  • the complete OHSU Beat AML cohort represents sample collection and characterization over a span of 10 years.
  • the initial cohort first reported in (Tyner et al., Nature 562, 526-531 , 2018) represent the first two waves of patient accrual and sample data (denoted “Waves 1 +2"). Additional longitudinal samples for Waves 1 +2, in addition to new patient accrual represents the final two waves (“Waves 3+4"). Harmonization of these datasets together for specific analyses was denoted as the “harmonized data”.
  • a specimens from the first available timepoint (defined by a 5-day interval) for each patient was utilized such that each patient was represented by a single sample for a given datatype. Additionally, samples taken while a patient was in remission were removed from consideration.
  • ESN European Leukemia Network Prognostic Categorization. Some updates were implemented to the pipeline for calling ELN categories. First, calls in this study were confined to specimens taken at initial acute leukemia diagnosis, and did not place specimens that were taken in remission, at relapse, or from cases with a non-AML diagnosis (e.g. MDS) into ELN categories. Second, for consideration of mutation of ASXL 1, RUNX1, or TP53, internal deep sequencing data were used as the primary source of information, rather than clinical sequencing results curated from the electronic medical record. This was due to a higher percentage of samples with available sequencing data for these three genes from the internal versus curated clinical data.
  • MDS non-AML diagnosis
  • Targetome Blucher et al., Trends in pharmacological sciences 38, 1085-1099, 2017; Blucher et al., F1000 Research 8, 908, 2019; Choonoo et al., PLoS One 14, e0223639, 2019
  • KINOMEscan Diavis et al., Nat Biotechnol 29, 1046-1051 , 2011 .
  • a threshold was used that was 10-fold higher than the second lowest Kd for each drug (termed Tierl hits).
  • Targetome was required to have at least two references with at least two unique supporting assay values. This high confidence set was combined with a small number of additional interactions annotated manually.
  • the IUPHAR family classifications for targets were utilized (Armstrong et al., Nucleic Acids Res 48, D1006-D1021 , 2020). Targets were assigned to the lowest level of the family hierarchy as well as up to two higher levels. The resulting inhibitor/family relationships were additionally manually curated.
  • Inhibitor family response To generate gene family scores, the inhibitor AUC responses were first rescaled to be between 0 and 1 in order to ensure comparability between inhibitors with different concentration ranges.
  • the single sample GSEA ssGSEA; Barbie etal., Nature 462, I OS- 112, 2009
  • GSVA package Hazelmann etal., BMC bioinformatics 14, 7, 2013
  • At least 5 inhibitors per family were required and the cohort range normalization was not implemented as only a single patient was provided at a time. Note that the use of ssGSEA as opposed to a principal component-based scoring was driven by the need to account for differences in drug coverage amongst patients.
  • RNA Sequencing Gene-level RNASeq counts were generated as in the previous Beat AML manuscript (Tyner et al., Nature 562, 526-531 , 2018). Again, conditional quantile normalization was used (CQN; Hansen etal., Biostatistics 13, 204-216, 2012). In order to facilitate comparison with previous results, the CQN reference distribution parameters learned from the Waves 1 +2 samples were used to apply the normalization to the Waves 3+4 samples.
  • WGCNA Weighted Gene Co-expression Network Analysis
  • the PC scores of the new cohort for each module were able to be directly ‘predicted’.
  • the new cohort expression matrix was centered and scaled (C/S) using the mean and standard deviations estimated from the original cohort. Scores per new cohort patient were then formed as the linear combination of their C/S expression values and the corresponding column of the original matrix of eigenvectors/rotations.
  • Module Membership A standard WGCNA methodology is the formation of kME values which are defined as the correlation of gene expression with the module eigengene (PC1 score as described herein) (Langfelder et al., Bioinformatics 24, 719-720, 2008). Genes with high kME are considered to have higher (fuzzy) membership in each module or alternatively can be seen as more ‘hub-like’ in a network context (Horvath and Dong, 2008). In this instance the correlation used was the robust biweight midcorrelation (Langfelder & Horvath, J Statistical Software 46, 2012).
  • Module Associations To relate WGCNA modules to external continuous variables, direct correlation of a variable with a module eigengene was assessed, which indicates whether the pattern of a module covaries with the variable (Horvath et al., Proc Natl Acad Sci U S A 103, 17402-17407, 2006).
  • Random Survival Forest Using the combined set of genomic features, a conditional random forest (cforest) model was fit using the 'partykit' package with post-diagnosis survival, measured in days, as the outcome (Hothorn et al., J Computa Graph Stat 15, 651 -674, 2006). The model utilized 1 ,000 trees and 63.2% subsampling as opposed to the bootstrap as previously suggested (Strobl et al., BMC Bioinformatics 8, 25, 2007). The predictions (i.e. Iog10 median survival) from this model were used to fit a single surrogate conditional inference tree model to facilitate interpretation (Pearson’s correlation between model predictions: .877).
  • LSC17 For comparisons with the LSC17 signature (Ng etal., Nature 540, 433-437, 2016), the signature was replicated using the coefficients in the manuscript, which was reported as the optimized signature by the authors (which was also described in WO2017132749A1 ). Categorization of expression for Hazard Ratio and other analyses was done using two different “split types”: median as described in (Ng et al., Nature 540, 433-437, 2016) or ctree to facilitate comparison with PEAR1.
  • NLP Natural Language Processing
  • partitions 1 and 2 were treated as a single training set and partitions 3, 4, and 5 as a single test set.
  • the training set consisted of 108 patients with 134 specimens and 241 documents
  • the test set consisted of 252 patients with 289 specimens and 532 documents.
  • Evaluation of NLP results led in some cases to discovery of missing or incorrect data in the GSDS (which was estimated to have an overall error rate of 9%), further improving data quality.
  • Accuracy ranged from 79% to 93%, Precision 85% to 96%, Recall from 76% to 93% and F1 -score (the harmonic mean of the precision and recall) 81 % to 94%.
  • amino acid changes in the protein variants disclosed herein are conservative amino acid changes, i.e., substitutions of similarly charged or uncharged amino acids.
  • a conservative amino acid change involves substitution of one of a family of amino acids which are related in their side chains.
  • Functional variants include one or more residue additions or substitutions that do not substantially impact the physiological effects of the protein.
  • Functional fragments include one or more deletions or truncations that do not substantially impact the physiological effects of the protein. A lack of substantial impact can be confirmed by observing experimentally comparable results in an activation study or a binding study.
  • Functional variants and functional fragments of intracellular domains e.g., intracellular signaling domains
  • Functional variants and functional fragments of binding domains bind their cognate antigen or ligand at a level comparable to a wild-type reference.
  • Naturally occurring amino acids are generally divided into conservative substitution families as follows: Group 1 : Alanine (Ala), Glycine (Gly), Serine (Ser), and Threonine (Thr); Group 2: (acidic): Aspartic acid (Asp), and Glutamic acid (Glu); Group 3: (acidic; also classified as polar, negatively charged residues and their amides): Asparagine (Asn), Glutamine (Gin), Asp, and Glu; Group 4: Gin and Asn; Group 5: (basic; also classified as polar, positively charged residues): Arginine (Arg), Lysine (Lys), and Histidine (His); Group 6 (large aliphatic, nonpolar residues): Isoleucine (lie), Leucine (Leu), Methionine (Met), Valine (Vai) and Cysteine (Cys); Group 7 (uncharged polar): Tyrosine (Tyr), Gly, Asn, Gin, Cys, Ser, and Thr
  • the hydropathic index of amino acids may be considered.
  • the importance of the hydropathic amino acid index in conferring interactive biologic function on a protein is generally understood in the art (Kyte and Doolittle, 1982, J. Mol. Biol. 157(1 ), 105-32).
  • Each amino acid has been assigned a hydropathic index on the basis of its hydrophobicity and charge characteristics (Kyte and Doolittle, 1982).
  • an amino acid can be substituted for another having a similar hydrophilicity value and still obtain a biologically equivalent, and in particular, an immunologically equivalent protein.
  • substitution of amino acids whose hydrophilicity values are within ⁇ 2 is preferred, those within ⁇ 1 are particularly preferred, and those within ⁇ 0.5 are even more particularly preferred.
  • amino acid substitutions may be based on the relative similarity of the amino acid side-chain substituents, for example, their hydrophobicity, hydrophilicity, charge, size, and the like.
  • variants of gene sequences can include codon optimized variants, sequence polymorphisms, splice variants, and/or mutations that do not affect the function of an encoded product to a statistically-significant degree.
  • Variants of the protein, nucleic acid, and gene sequences disclosed herein also include sequences with at least 70% sequence identity, 80% sequence identity, 85% sequence, 90% sequence identity, 95% sequence identity, 96% sequence identity, 97% sequence identity, 98% sequence identity, or 99% sequence identity to the protein, nucleic acid, or gene sequences disclosed herein.
  • % sequence identity refers to a relationship between two or more sequences, as determined by comparing the sequences.
  • identity also means the degree of sequence relatedness between protein, nucleic acid, or gene sequences as determined by the match between strings of such sequences.
  • Identity (often referred to as “similarity") can be readily calculated by known methods, including (but not limited to) those described in: Computational Molecular Biology (Lesk, A. M., ed.) Oxford University Press, NY (1988); Biocomputing: Informatics and Genome Projects (Smith, D. W., ed.) Academic Press, NY (1994); Computer Analysis of Sequence Data, Part I (Griffin, A. M., and Griffin, H.
  • Variants also include nucleic acid molecules that hybridizes under stringent hybridization conditions to a sequence disclosed herein and provide the same function as the reference sequence.
  • Exemplary stringent hybridization conditions include an overnight incubation at 42 °C in a solution including 50% formamide, 5XSSC (750 mM NaCI, 75 mM trisodium citrate), 50 mM sodium phosphate (pH 7.6), 5X Denhardt's solution, 10% dextran sulfate, and 20 pg/ml denatured, sheared salmon sperm DNA, followed by washing the filters in 0.1 XSSC at 50 °C.
  • 5XSSC 750 mM NaCI, 75 mM trisodium citrate
  • 50 mM sodium phosphate pH 7.6
  • 5X Denhardt's solution 10% dextran sulfate
  • Changes in the stringency of hybridization and signal detection are primarily accomplished through the manipulation of formamide concentration (lower percentages of formamide result in lowered stringency); salt conditions, or temperature.
  • washes performed following stringent hybridization can be done at higher salt concentrations (e.g. 5XSSC).
  • Variations in the above conditions may be accomplished through the inclusion and/or substitution of alternate blocking reagents used to suppress background in hybridization experiments.
  • Typical blocking reagents include Denhardt's reagent, BLOTTO, heparin, denatured salmon sperm DNA, and commercially available proprietary formulations.
  • the inclusion of specific blocking reagents may require modification of the hybridization conditions described above, due to problems with compatibility.
  • each embodiment disclosed herein can comprise, consist essentially of or consist of its particular stated element, step, ingredient or component.
  • the terms “include” or “including” should be interpreted to recite: “comprise, consist of, or consist essentially of.”
  • the transition term “comprise” or “comprises” means has, but is not limited to, and allows for the inclusion of unspecified elements, steps, ingredients, or components, even in major amounts.
  • the transitional phrase “consisting of” excludes any element, step, ingredient or component not specified.
  • the transition phrase “consisting essentially of” limits the scope of the embodiment to the specified elements, steps, ingredients or components and to those that do not materially affect the embodiment. A material effect would cause a statistically significant reduction in the ability to use PEAR1 as a prognostic biomarker for subjects ⁇ 45 years old having AML.
  • the term “about” has the meaning reasonably ascribed to it by a person skilled in the art when used in conjunction with a stated numerical value or range, i.e. denoting somewhat more or somewhat less than the stated value or range, to within a range of ⁇ 20% of the stated value; ⁇ 19% of the stated value; ⁇ 18% of the stated value; ⁇ 17% of the stated value; ⁇ 16% of the stated value; ⁇ 15% of the stated value; ⁇ 14% of the stated value; ⁇ 13% of the stated value; ⁇ 12% of the stated value; ⁇ 11 % of the stated value; ⁇ 10% of the stated value; ⁇ 9% of the stated value; ⁇ 8% of the stated value; ⁇ 7% of the stated value; ⁇ 6% of the stated value; ⁇ 5% of the stated value; ⁇ 4% of the stated value; ⁇ 3% of the stated value; ⁇ 2% of the stated value; or ⁇ 1% of the stated value.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Immunology (AREA)
  • Engineering & Computer Science (AREA)
  • Organic Chemistry (AREA)
  • Molecular Biology (AREA)
  • Pathology (AREA)
  • Analytical Chemistry (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Hematology (AREA)
  • Zoology (AREA)
  • General Health & Medical Sciences (AREA)
  • Biotechnology (AREA)
  • Microbiology (AREA)
  • Urology & Nephrology (AREA)
  • Hospice & Palliative Care (AREA)
  • Wood Science & Technology (AREA)
  • Genetics & Genomics (AREA)
  • Biochemistry (AREA)
  • Biomedical Technology (AREA)
  • Physics & Mathematics (AREA)
  • Oncology (AREA)
  • General Engineering & Computer Science (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Cell Biology (AREA)
  • Biophysics (AREA)
  • Food Science & Technology (AREA)
  • Medicinal Chemistry (AREA)
  • General Physics & Mathematics (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
  • Investigating Or Analysing Biological Materials (AREA)

Abstract

Acute myeloid leukemia (AML) is a cancer of neoplastic myeloid-lineage cells. Conventional therapeutic approaches have had limited success with recently approved drug regimens offering transient improvements. Integration of genomic data, transcriptomic data, ex vivo drug sensitivity data, and clinical annotations for a large cohort of AML patient samples show that drug sensitivity is governed broadly by AML cell differentiation state. Platelet endothelial aggregation receptor 1 (PEAR1) was identified as a biomarker that can predict clinical outcome in AML patients < 45 years old. Methods of diagnosis, prognosis, and treatment are described.

Description

BIOMARKERS FOR ACUTE MYELOID LEUKEMIA AND USES THEREOF
INCORPORATION BY REFERENCE TO ANY PRIORITY APPLICATIONS
[0001] This application claims priority to and the benefit of the earlier filing of U.S. Provisional Application No. 63/307,567, filed on February 7, 2022, which is incorporated by reference herein in its entirety.
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT [0002] This invention was made with government support under grant numbers CA217862 and CA224019 awarded by National Institutes of Health/National Cancer Institute. The government has certain rights in the invention.
FIELD OF THE DISCLOSURE
[0003] The present disclosure relates to biomarkers for acute myeloid leukemia (AML) and their use, including to predict outcome for subjects with this cancer and to direct treatment toward better outcomes.
BACKGROUND OF THE DISCLOSURE
[0004] Acute myeloid leukemia (AML) is an aggressive hematologic malignancy characterized by an aberrant proliferation of immature blast cells in peripheral blood and bone marrow that leads to ineffective production of red blood cells and bone marrow failure. The number of new cases among men and women per year is 4.2 per 100,000 population. In the United States, the incidence is over 20,000 cases per year. The average age at the time of diagnosis is about 65 years. Complex genetic and biological features including chromosomal abnormalities and gene mutations contribute to frequent drug resistance and disease relapse. T reatment for AML includes chemotherapies that have been in use for several decades and hematopoietic stem cell/bone marrow transplant. However, durable remission is only achieved for a small percentage of patients.
[0005] AML is a heterogeneous disease with thousands of somatically mutated genes and numerous chromosomal abnormalities. Gene mutations include single point mutations, insertions, deletions, and duplications. Chromosomal abnormalities include these gene mutations and cytogenetic events have been shown to have prognostic significance.
[0006] Understanding of the genetics of AML has led to development of therapeutics targeted to address the underlying molecular changes associated with disease. The therapeutics include small molecules and an antibody drug conjugate targeting CD33 (gemtuzumab) for newly diagnosed or relapsed AML expressing CD33.
[0007] All of these treatments have led to improvements in initial remission rates. However, drug resistance and disease relapse remain a persistent problem. Biomarkers that can predict the outcome of AML can help direct better treatment for subjects.
SUMMARY OF THE DISCLOSURE
[0008] The current disclosure provides biomarkers for acute myeloid leukemia (AML). The biomarkers can be used to predict clinical outcomes for subjects with this disease and to direct treatment for better outcomes.
[0009] Embodiments provide for a method for determining the prognosis (or diagnosis) for a subject < 45 years old having AML, including: obtaining a biological sample derived from the subject; measuring in the biological sample a level of platelet endothelial aggregation receptor 1 (PEAR1 ); comparing the measured level of PEAR1 to a threshold level; and assigning to the subject a poor prognosis when the measured level of PEAR1 is greater than the threshold level. In embodiments, the method does not include measuring in the biological sample a level of pyrin domain (PYD) and caspase activation and recruitment domain (CARD) containing (PYCARD) gene or protein. In embodiments, the threshold level includes 0.329 PEAR1 RNA reads per kilobase of transcript per million total reads (RPKM). In embodiments, the biological sample includes peripheral blood or bone marrow aspirate. In embodiments, the subject has one or more mutations or gene rearrangements in TP53, RUNX1, ASXL1, SRSF2, or GATA2-MECOM. In embodiments, the method further includes performing a stem cell transplant for the subject. In embodiments, the treatment regimen for the subject can be modified based on the prognosis.
[0010] Embodiments also provide for a method of treating a subject < 45 years old having AML, including administering a therapeutically effective amount of a drug that reduces or eliminates expression of PEAR1 or includes an antagonist of PEAR1 function. In embodiments, the drug includes a nucleic acid. In embodiments, the drug includes an inhibitor of an integrin subunit. In embodiments, the drug includes eptifibatide. In embodiments, the drug includes an antibody, or binding fragment thereof that binds to PEAR1 .
[0011] Embodiments also provide for a method for identifying a subject having AML who is responsive or resistant to a drug based on their AML cell differentiation state and/or mutational status. BRIEF DESCRIPTION OF THE DRAWINGS
[0012] FIG. 1 Influence of cell-type on inhibitor response. Drug response modification was quantified by examining the significance (Y-axis) of the interaction between each mutational event and cell-type (x-axis) for each inhibitor, requiring a minimum of 10 mutations. Interactions with q value at two thresholds (q < 0.1 ; q < 0.05) are called out by text and shape and distinguished by a dashed and solid line, respectively. Mutation-driven drug response modification of the relationship between inhibitor AUC and cell-type largely occurs within the more-differentiated celltypes and is driven by FLT3-ITD and NRAS. The conditional relationships between sorafenib, FLT3-ITD, and cell-type scores are shown in Figure S3B of Bottomly et al., 2022.
[0013] FIGs. 2A-2B. Genomic associations of drug family response. (FIG. 2A) Family-level drug response summaries allow identification of cohort-level responses. Some like the Aurora Kinase family (Aurk) show overall resistance and others like Type XIII RTKs (Eph) or PIKK are overall sensitive. (FIG. 2B) Mutational status associates with drug family response either in terms of significant (q value < 0.05; Storey et al., Proc Natl Acad Sci U S A 100, 9440-9445, 2003) sensitivity (left of dashed line) or resistance (right of dashed line). Association tests were performed using Welch’s T-test comparing mutated vs wild type and requiring at least 5 mutations for a given mutational event. Effect size was based off of Glass’s delta using wild type as the reference.
[0014] FIG. 3 Drug family response is influenced by and conditional on cell-type. Mutations that play a significant drug response modification role can be determined, based on the statistical interaction between cell-type score and mutation, requiring at least 10 mutations per event. The Y-axis indicates the signed -Iog10 (P value), the X-axis indicates cell-type. The interactions are listed by text (q value < 0.1 and q value < 0.05; Storey etal., Proc Natl Acad Sci U S A 100, 9440- 9445, 2003) and distinguished by a dashed and solid line, respectively.
[0015] FIGs. 4A-4C. Integrative modeling shows PEAR1 expression is a single, independent predictor of poor prognosis, particularly in younger patients and performs similar to LSC17. (FIG. 4A) PEAR1 expression (y-axis) is significantly higher in Adverse compared to Favorable ELN2017 risk categorization (x-axis) for the patients 60 and over (dots; older + oldest group) but the pattern is less pronounced in those patients < 60. Significance determined using a using a Welch’s T- test. (FIG. 4B) PEAR1 expression differentiates survival for young patients equivalently to the LSC17 signature for patients with shorter survival times. Categorization of samples into high (oval) and low (triangle) expression groups for PEAR1 and LSC17 was determined using the ctree methodology to facilitate comparison with PEARI . (FIG. 4C) Hazard ratios (HRs) and 95% confidence intervals (Cis) for PEAR1 and LSC17 in the entire cohort or in the younger patients as well as in all sample types or only in bone marrow samples. The categories were high vs low expression as determined by either median threshold or significant splits using ctree (i.e., split type).
[0016] FIGs. 5A-5D. PEAR1 has prognostic ability in entire cohort. PEAR1 performs similarly to the LSC17 signature when using the entire cohort where specimens were obtained at initial acute leukemia diagnosis with splitting defined using our conditional inference tree methodology for both (FIG. 5A, 5B) or split based on the median values for both (FIG. 5C, 5D) (as described in Ng et al., Nature 540, 433-437, 2016, for LSC17).
STATEMENT REGARDING SEQUENCE LISTING
[0017] The nucleic acid and/or amino acid sequences described herein are shown using standard letter abbreviations, as defined in 37 C.F.R. §1.822. Only one strand of each nucleic acid sequence is shown, but the complementary strand is understood as included in embodiments where it would be appropriate. A computer readable text file, entitled "0046- 0064PCT_SeqList.xml" created on or about February 6, 2023, with a file size of 12 KB, contains the sequence listing for this application and is hereby incorporated by reference in its entirety.
[0018] In the provided Sequence Listing:
[0019] SEQ ID NO: 1 is the amino acid sequence of Homo sapiens PEAR1 (UniProt ID Q5VY43). [0020] SEQ ID NO: 2 is the nucleic acid sequence encoding Homo sapiens PEAR1 (nt 130-3243 of Gen Bank NM_001080471 .3).
[0021] SEQ ID NO: 3 is the nucleic acid sequence of forward primer FLT3.
[0022] SEQ ID NO: 4 is the nucleic acid sequence of reverse primer FLT3.
[0023] SEQ ID NO: 5 is the nucleic acid sequence of forward primer NPM1 .
[0024] SEQ ID NO: 6 is the nucleic acid sequence of reverse primer NPM1 .
DETAILED DESCRIPTION
[0025] Acute myeloid leukemia (AML) is an aggressive hematologic malignancy characterized by an aberrant proliferation of neoplastic myeloid-lineage cells. Approximately 21 ,000 people are diagnosed with AML and over 10,000 AML-related deaths are reported annually in the United States (Jemal et al., CA: A Cancer Journal for Clinicians 60, 277-300, 2010; SEER, available online at seercancergov/statfacts/html/amylhtml, 2021 ). This urgent, unmet clinical need is driven by complex genetic and biological features that fuel frequent drug resistance and disease relapse. Genetic features include 16 recurrent gene rearrangements or copy number variations as well as a plethora of unique, tumor-specific aberrations (Arber et al., Blood 127, 2391 -2405, 2016). In addition, large studies have collectively revealed 60 genes with recurrent point mutations, with many thousand additional rarely mutated genes (Cancer Genome Atlas Research et al., 2013; Papaemmanuil et al., N Engl J Med 374, 2209-2221 , 2016; Tyner et al., Nature 562, 526-531 , 2018). Conventional chemotherapies that have been in use for several decades combine anthracyclines with nucleoside analogs and a subsequent bone marrow transplant for 20% of patients. This results in durable remission for only 20% of patients (Pulte et al., Intern. J Cancer 139, 1289-1296, 2016).
[0026] In addition to the genetic heterogeneity of AML, these tumors also exhibit a diversity of cellular phenotypes that can be aligned with differentiation states observed in healthy hematopoiesis. These phenotypes have been captured in diagnostic classification schemes such as the French-American-British (FAB) system (Bennett et al., Br J Haematol 33, 451 -458, 1976) as well as diagnostic subsets from the World Health Organization that are based on tumor cell maturation state (Arber etal., Blood V2.7, 2391 -2405, 2016). Significant work has also been done to define the most primitive AML cell state, leukemic stem cells (LSC), and to understand gene expression signatures that define LSCs, which have been shown to carry prognostic significance in AML and myelodysplastic syndromes (MDS) (Elsayed etal., Leukemia 34, 735-745, 2020; Gal et al., Leukemia 20, 2147-2154, 2006; Gentles et al., JAMA 304, 2706-2715, 2010; Horibata et al., Proc Natl Acad Sci U S A 116, 10494-10503, 2019; Ng et al., Nature 540, 433-437, 2016; Wang et al., Blood Advances 4, 644-654, 2020). Most recently, single-cell sequencing has been employed to describe gene expression signatures that define six distinct AML tumor maturation states (van Galen et al., Cell 176, 1265-1281 e1224, 2019).
[0027] Collectively, this understanding of genetic and cellular features of AML has led to a number of rationally targeted therapeutics including all-trans retinoic acid combined with arsenic trioxide for AML with rearrangement of the retinoic acid receptor (Huang et al., Blood 72, 567-572, 1988; Shen et al., Blood 89, 3354-3360, 1997), small-molecules targeting mutated enzymes such as FLT3 (gilteritinib (Perl et al., N Engl J Med 381 , 1728-1740, 2019) and midostaurin (Stone et al., N Engl J Med 377, 454-464, 2017)) or IDH1/2 (ivosidenib (DiNardo et al., N Engl J Med 378, 2386-2398, 2018) and enasidenib (Stein et al., Blood 130, 722-731 , 2017)), an antibody drug conjugate targeting CD33 (gemtuzumab (Petersdorf etal., Blood 121 , 4854-4860, 2013) for newly diagnosed or relapsed AML expressing CD33, a liposomal formulation of cytarabine and daunorubicin (CPX-351 ; Lancet etal., J Clin Oncol 36, 2684-2692, 2018) for therapy-related AML or AML with myelodysplasia-related changes, and the BCL2 inhibitor, venetoclax, used in combination with hypomethylating agents (azacytidine or decitabine), for elderly patients not fit for standard chemotherapy (DiNardo etal., Blood 133, 7-17, 2019). All of these approaches have led to improvements in initial remission rates; however, drug resistance and disease relapse remain a persistent problem necessitating a more complete understanding of the biological factors driving drug response.
[0028] The present disclosure describes the combining of ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients. The analysis identifies associations of drug response with AML cell differentiation state and/or mutation status. Modeling of clinical outcome reveals a single gene, PEAR1 , to be among the strongest predictors of patient survival, especially for young patients.
[0029] Aspects of the current disclosure are now described with additional details and options as follows: (I) Biomarkers; (II) Biological samples; (III) Platelet endothelial aggregation receptor 1 (PEAR1 ); (IV) Mutational status; (V) Drug families and drugs for AML; (VI) AML cell differentiation states; (VII) Methods of Use; (VIII) Exemplary Embodiments; (IX) Experimental Examples; (X) Variants; and (XI) Closing Paragraphs. These headings do not limit the interpretation of the disclosure and are provided for organizational purposes only.
(I) Biomarkers.
[0030] A biomarker refers to molecular, biological or physical attributes that characterize a physiological, cellular, or disease state and that can be objectively measured to detect or define disease progression or predict or quantify therapeutic responses. A biomarker includes a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. In embodiments, a biomarker can be any molecular structure produced by a cell or organism. In embodiments, a biomarker can be expressed inside any cell or tissue, accessible on the surface of a tissue or cell, structurally inherent to a cell or tissue such as a structural component, secreted by a cell or tissue, produced by the breakdown of a cell or tissue through processes such as necrosis, apoptosis or the like, or a combination thereof. In embodiments, a biomarker may be any protein, carbohydrate, fat, nucleic acid, catalytic site, or any combination of these such as an enzyme, glycoprotein, cell membrane, virus, cell, organ, organelle, or any uni- or multimolecular structure or any other such structure now known or yet to be disclosed whether alone or in combination. In embodiments, a biomarker can be a receptor expressed on a surface of a cell, such as PEAR1 . Because it is present on the surface of a cell, PEAR1 is particularly useful as a biomarker due to its accessibility. It is also useful for instance for cell sorting and other actions that rely on accessibility of the target protein from the outside of a cell. [0031] In embodiments, biomarkers disclosed herein can be used to determine the prognosis for a subject < 45 years old having AML. In embodiments, biomarkers disclosed herein can be used to modify the treatment regimen for a subject < 45 years old having AML to improve prognosis.
[0032] Assays known to one of skill in the art can be used to measure a level of a biomarker. For example, the quantity of one or more biomarkers can be indicated as a value. The value can be expressed numerically and result from assaying a sample, and can be derived, e.g., by measuring level(s) of the biomarker(s) in the sample by an assay performed in a laboratory, by measuring the ratio or ratios of the levels of two or more biomarkers, or from a dataset obtained from a provider such as a laboratory, or from a dataset stored on a server. The value may be qualitative or quantitative. As such, where detection is qualitative, the systems and methods provide a reading or evaluation, e.g., assessment, of whether or not the biomarker is present in the sample being assayed. In embodiments, the systems and methods provide a quantitative detection of whether the biomarker is present in the sample being assayed, i.e., an evaluation or assessment of the actual amount or relative abundance of the biomarker in the sample being assayed. In such embodiments, the quantitative detection may be absolute or, if the method is a method of detecting two or more different biomarkers in a sample, relative. As such, the term “quantifying” when used in the context of quantifying a biomarker in a sample can refer to absolute or to relative quantification. Absolute quantification can be accomplished by inclusion of known concentration(s) of one or more control biomarkers and referencing, e.g., normalizing, the detected level of the biomarker with the known control biomarkers {e.g., through generation of a standard curve). Alternatively, relative quantification can be accomplished by comparison of detected levels or amounts between two or more different biomarkers to provide a relative quantification of each of the two or more biomarkers, e.g., relative to each other. The actual measurement of values of the biomarkers can be determined using any method known in the art. In some embodiments, a biomarker is detected by contacting a sample with reagents e.g., antibodies or nucleic acids), generating complexes of reagent and biomarker(s), and detecting the complexes.
[0033] The reagent can include a probe. A probe is a molecule that binds a target, either directly or indirectly. The target can be a biomarker, a fragment of a biomarker, or any molecule that is to be detected. In embodiments, the probe includes a nucleic acid or a protein. As an example, a protein probe can be an antibody. An antibody can be a whole antibody or a binding fragment of an antibody. A probe can be labeled with a detectable label. Examples of detectable labels include fluorescent chromophores, chemiluminescent emitters, dyes, enzymes, enzyme substrates, enzyme cofactors, enzyme inhibitors, enzyme subunits, metal ions, and radioactive isotopes. [0034] "Protein" detection includes detection of full-length proteins, mature proteins, pre-proteins, polypeptides, isoforms, mutant forms, post-translationally modified proteins and variants thereof, and can be detected in any suitable manner.
[0035] Those skilled in the art will be familiar with numerous specific immunoassay formats and variations thereof which can be used to carry out the methods disclosed herein. See, e.g., E. Maggio, Enzyme-Immunoassay (1980), CRC Press, Inc., Boca Raton, Fla; and U.S. Pat. Nos. 4,727,022; 4,659,678; 4,376,1 10; 4,275,149; 4,233,402; and 4,230,797.
[0036] Antibodies can be conjugated or immobilized to a solid support suitable for a diagnostic assay (e.g., beads such as protein A or protein G agarose, microspheres, plates, slides or wells formed from materials such as latex or polystyrene) in accordance with known techniques, such as passive binding. Antibodies can be conjugated to detectable labels or groups such as radioisotopes (e.g., 35S, 125l, 1311), enzyme labels (e.g., horseradish peroxidase, alkaline phosphatase), and fluorescent labels (e.g., fluorescein, Alexa, green fluorescent protein, rhodamine) in accordance with known techniques.
[0037] Examples of suitable immunoassays include immunoblotting, immunoprecipitation, immunofluorescence, chemiluminescence, electro-chemiluminescence (ECL), and/or enzyme- linked immunosorbent assays (ELISA).
[0038] In embodiments, the transcript level of a biomarker can be measured by RNA sequencing (RNA-seq). A protocol for RNA sequencing is described in Tyner et al. (Nature 562, 526-531 , 2018). Briefly, poly(A)+ RNA from an AML sample is chemically fragmented. Double stranded cDNAs are synthesized using random hexamer priming with 3’ ends of the cDNA adenylated and then indexed adaptors are ligated. Library amplification is performed using three-primer PCR using a uracil DNA glycosylase addition for strandedness. Libraries are validated with the Bioanalyzer (Agilent) and combined to run 4 samples per lane, with a targeted yield of 200 million clusters. Combined libraries are denatured, clustered (e.g., with the cBot (Illumina)), and sequenced on a next generation sequencing (NGS) machine using a 100-cycle paired end protocol. In embodiments, RNA from purified CD34+ cells from healthy control bone marrow can serve as control RNA. In embodiments, the control RNA can serve as both a healthy comparator and a quality check on inter-group batch effects. In embodiments, RNA from whole mononuclear bone marrow cells from healthy donors can also be included as control RNA. In embodiments, RNA reads can be mapped and aligned to a reference genome such as GRCh37. All genes with no counts across the samples are excluded. Genes with duplicate gene symbols and those where the counts are < 10 for 90% or more of the samples are additionally removed prior to normalization similar to the approach suggested for weighted gene correlation network analysis (WGNCA) (Langfelder & Horvath (2008) BMC bioinformatics 9:559). Samples for which their median expression is less than 2 standard deviations below the average are removed from the dataset. Normalization is performed using the conditional quantile normalization procedure (Hansen etal., Biostatistics 13, 204-216, 2012), which produces GC-content corrected Iog2 reads per kilobase of transcript per million mapped reads (RPKM) values. In embodiments, RPKM values referred to herein include conditional quantile normalization (CQN)-normalized RPKM. One of skill in the art will recognize that a transcript level can be expressed in other art-recognized units, such as TPM (transcripts per million), FPKM (fragments per kilobase of transcript per million fragments mapped), and CPM (counts per million). FPKM is analogous to RPKM and is used for normalizing counts for paired-end RNA-seq data in which two reads are sequenced for each DNA fragment. Counts per million mapped reads are counts scaled by the number of sequenced fragments multiplied by one million. Transcripts per million (TPM) is a measurement of the proportion of transcripts in a pool of RNA.
[0039] Biomarker expression thresholds for patient stratification are based on the cohort utilized for development of a biomarker signature. Optimization of both the threshold and expression reporter platform can be easily done for clinical populations for transition from research to clinical care by a person of skill in the art for Clinical Laboratory Improvement Amendments (CLIA)- certified laboratory validation.
(II) Biological samples.
[0040] "Sample" or "biological sample" refers to a biological material isolated from or derived from a subject. Embodiments of “derived from” refer to a biological sample being obtained from a subject or other source and including any modification to the sample, addition to the sample, or removal from the sample, as long as biomarkers of the present disclosure can be measured from the sample using the systems and methods of the present disclosure. The biological sample can contain any biological material suitable for detecting a mRNA, polypeptide or other marker of a physiologic or pathologic process in a subject, and can include fluid, tissue, cellular and/or non- cellular material obtained from the individual. In embodiments, a biological sample can include blood, serum, cells, plasma, cerebral spinal fluid, and urine. In embodiments, a biological sample can include serum. Serum from blood is a light yellow, clear liquid that remains after blood has clotted. Serum can be obtained by centrifuging clotted blood. Serum does not include an anticoagulant. In embodiments, a biological sample can include plasma. Plasma is a light yellow, clear liquid that remains when blood clotting is prevented and can be obtained by centrifuging whole blood containing an anti-coagulant. [0041] In embodiments, a biological sample may include cells. In embodiments, samples used in the methods of the present disclosure include mononuclear cells isolated from peripheral blood (PBMCs) or from bone marrow aspirates. Mononuclear cells can be isolated by any technique known in the art, including density centrifugation (e.g., with Ficoll-Paque). Density gradient centrifugation separates cells by cell density. In embodiments, PBMC can be isolated by leukapheresis. A leukapheresis machine is an automated device that takes whole blood from a donor and separates out the target PBMC fraction using high-speed centrifugation while returning the remaining portion of the blood, including plasma, red blood cells, and granulocytes, back to the donor.
(Ill) Platelet endothelial aggregation receptor 1 (PEAR1).
[0042] PEAR1 , also known as JEDI or MEGF12, was originally identified through an investigation of membrane proteins that signal during platelet aggregation (Nanda et al., J Biol Chem 280, 24680-24689, 2005) and is a homolog of Draper (D. melanogaster) and ced-1 (C. elegans). It is a type I transmembrane protein with 15 extracellular epidermal growth factor-like repeats, a domain sharing homology with NOTCH ligands, and it contains multiple tyrosine and serine residues that become phosphorylated upon forced platelet aggregation or prolonged phosphatase inhibition (Krivtsov et al., J Cell Biochem 101 , 767-784, 2007; Nanda et al., J Biol Chem 280, 24680-24689, 2005). Tyrosine phosphorylation occurring through thrombin- or collagen-induced platelet aggregation was shown to be blocked with eptifibatide, an integrin subunit alpha 2b/beta 3 antagonist (Nanda et al., J Biol Chem 280, 24680-24689, 2005). Numerous studies have examined genetic polymorphisms within the PEAR1 locus and associations between these variants and cardiovascular events as well as response to anti-platelet aggregation therapies (Eicher et al., Am J Hum Genet 99, 40-55, 2016; Faraday et al., Blood 118, 3367-3375, 2011 ; Herrera-Galeano et al., Arterioscler Thromb Vase Biol 28, 1484-1490, 2008; Johnson et al., Nat Genet42, 608-613, 2010; Jones etal., Blood 1 14, 1405-1416, 2009; Lewis etal., Circ Cardiovasc Genet 6, 184-192, 2013). Methylation of the PEAR1 locus has also been examined and methylation state has been shown to correlate with megakaryopoiesis and platelet function (Izzi et al., Clin Epigenetics 11 , 151 , 2019; Izzi et al., Int J Mol Sci 19, 2018; Izzi et al., Blood 128, 1003-1012, 2016). PEAR1 expression is observed on endothelial cells and genetic variation and function of PEAR1 has also been shown to impact on endothelial cell biology (Fisch et al., PLoS One 10, e0138795, 2015; Vandenbriele et al., Cardiovasc Res 108, 124-138, 2015; Zhan et al., Microvasc Res 128, 103941 , 2020). On glial precursor cells, PEAR1 serves as a receptor to trigger clathrin-dependent engulfment of apoptotic neurons that are generated during the development of peripheral ganglia (Sullivan et al., Mol Biol Cell 25, 1925-1936, 2014; Wu et al., Nat Neurosci 12, 1534-1541 , 2009). The signaling underlying this phagocytic process has been studied in mammalian and D. melanogaster models and has been shown to involve SYK, SRC- family kinases, and MAPK8 (JNK; JUN N-terminal kinase) and/or their D. melanogaster homologs (Hilu-Dadia et al., Glia 66, 1520-1532, 2018; Scheib et al., J Neurosci 32, 13022-13031 , 2012). Several ligands have been identified for PEAR1 on platelets, including Fc Epsilon Receptor la (Sun et al., Mol Cell Proteomics 14, 1265-1274, 2015), dextran sulfate (Vandenbriele et al., Platelets 27, 365-372, 2016), and sulfated fucose-based polysaccharides (fucoidans) (Kardeby et al., Blood advances 3, 275-287, 2019), and signaling studies in platelets have also implicated SRC-family kinases in PEAR1 phosphorylation leading to activation of downstream PIK3C/AKT (Kauskot et al., Blood 119, 4056-4065, 2012; Kauskot et al., Blood 121 , 5208-5217, 2013). In healthy hematopoietic cells, PEAR1 expression is reported to be highest in HSC with decreasing expression as cells differentiate, with the exception that megakaryocyte-erythroid progenitor cells exhibit elevated PEAR1 expression. Forced expression of PEAR1 in bone marrow cells or in fibroblast stromal cells was shown to reduce clonogenic myeloid colony formation (Krivtsov etal., J Cell Biochem 101 , 767-784, 2007).
[0043] In embodiments, PEAR1 amino acid sequence includes SEQ ID NO: 1 (UniProt ID Q5VY43). In embodiments, PEAR1 is encoded by a sequence including SEQ ID NO: 2 (nt I SO- 3243 of Gen Bank reference sequence NM 001080471 .3).
[0044] Levels of PEAR1 can be detected at the transcript level or at the protein level using methods known in the art. In embodiments, a level of PEAR1 transcript, expressed as RPKM as described herein, can be measured.
[0045] Antibodies that can be used to detect biomarkers of the present disclosure are available commercially. In particular embodiments, antibodies to detect human PEAR1 include: a rabbit polyclonal antibody PA5-21057 (ThermoFisher), goat polyclonal AF4527 (Novus Biologicals), and mouse monoclonal MAB4527 (Novus Biologicals).
[0046] In embodiments, PEAR1 ELISAs are available commercially (ELH-PEAR1 -2, Ray Biotech).
(IV) Mutational status.
[0047] AML is a collection of neoplasms with heterogeneous pathophysiology, genetics and prognosis. Chromosomal abnormalities include chromosome translocations, monosomy, extra copies of chromosomes (e.g., trisomy), and deletion of part or all of a chromosome. In embodiments, chromosomal abnormalities include: deletions of part or all of chromosomes 5 or 7 (—5/— 7 AML); trisomy 8; the long arm of chromosome 11 (1 1q); balanced translocations between chromosomes 15 and 17 (t(15;17)); chromosomes 8 and 21 (t(8;21 )); others such as (q22;q22), (q31 ;q22), and t(9;1 1 ); and inversions such as inv(16).
[0048] Thirty percent of all patients with AML are currently classified based on specific abnormal karyotypes in groups with either good or bad prognosis. The remaining seventy percent of patients, however, are not classifiable because of the lack of cytogenetic markers.
[0049] Based on cytogenetics and molecular analysis, AML patients are presently classified into groups or subsets of AML with markedly contrasting prognosis. For instance, the genetic translocations inv( 16), t(8;21 ) and t(15;17) characterize AML with a relatively favorable prognosis, whereas the cytogenetically bad-risk leukemias include patients with abnormalities involving 11 q23, loss of 5(q) or 7(q), t(6;9) and t(9;22).
[0050] The following table shows representative chromosomal aberrations and corresponding gene fusions in AML.
Figure imgf000014_0001
[0051] A number of genes are observed to be mutated in AML and are associated with good or bad outcome for the disease. The most common molecular abnormality in AML is the internal tandem duplication (ITD) in the fms-like tyrosine kinase-3 gene (FLT3), a hematopoietic growth factor receptor. FLT3 ITD mutations confer a bad prognosis to AML patients. Point mutations and deletion of the tyrosine kinase domain (FLT3-TKD) are also seen in AML patients.
[0052] Transcription factor TP53 functions in cell cycle arrest for DNA mismatch repair, base excision repair, and nucleotide excision repair. Mutations in TP53 are prevalent in relapsed/refractory AML and in older patients with a much lower complete remission. Residual TP53 mutation is associated with resistance to chemotherapy. [0053] In embodiments, AML patients with biallelic mutations in the transcription factor CEBPA have been associated with good outcome. Biallelic mutations in CEBPA (biCEBPA) involve both mutations in N-terminal and C-terminal domains on separate alleles.
[0054] Mutated NPM1 is associated with a higher complete remission, improved overall survival, and a lower cumulative incidence of relapse. In embodiments, NPM1 can be a marker for minimal residual disease (MRD).
[0055] RUNX1 is a transcription factor that functions in hematopoiesis and is important for defining a hematopoietic stem cell. Chromosomal translocation t(8;21 ) results in a RUNX1 - RUNX1 T 1 fusion protein that promotes cell cycle progression. In embodiments, RUNX1 mutations are associated with inferior outcomes.
[0056] Mutations in IDH1 or IDH2 result in neomorphic enzymatic function and production of an oncometabolite, 2-hydroxyglutarate, which can lead to DNA hypermethylation, aberrant gene expression, cell proliferation, and abnormal differentiation. In embodiments, IDH1/2 mutations with NPM1 mutations are associated with improved outcome.
[0057] DNMT3A is a methyltransferase that is frequently mutated in AML patients. The enzyme functions in de novo methylation of CpG dinucleotides. Two-thirds of AML cases have mutations at R882 in exon 23 of DNMT3A which affects protein function.
[0058] KMT2A is a lysine-specific methyltransferase. Partial tandem duplications (PTD) in this gene along with other gene mutations or cytogenetic aberrations are associated with adverse outcomes.
[0059] ASXL1 is an epigenetic regulator and frequently mutated in AML. The presence of both ASXL1 and RUNX1 mutations is related to poor prognosis in AML patients.
[0060] The following table shows representative genes that are mutated in AML
Figure imgf000015_0001
Figure imgf000016_0001
[0061] In embodiments, a mutational status of a subject with AML can be determined by methods known in the art. Mutational status of a subject with AML includes any chromosomal abnormality (translocations, inversions, extra copies of chromosomes, absence of a member of a pair of chromosomes, deletion of a part or all of a chromosome) and gene mutations (single nucleotide polymorphisms, insertions, deletions, internal tandem duplication, partial tandem duplication). In embodiments, the chromosomal abnormalities and/or gene mutations are associated with genes known to be mutated in AML as described herein.
[0062] Methods to assess mutational status of a subject with AML include whole genome sequencing, whole exome sequencing, gene panels for hematologic malignancies, fluorescence in situ hybridization (FISH), PCR and electrophoresis, and RNA sequencing (RNA-seq). Wholegenome sequencing (WGS) involves creation of an in vitro library from a patient sample by fragmentation of genomic DNA and ligation of adaptors, and then sequencing the genomic fragments. After alignment with a reference genome, the sequence is analyzed for variants. Whole exome sequencing (WES) focuses on sequencing the regions of the genome that encode proteins. Sequencing can use one of many next generation sequencing (NGS) platforms and chemistries available including Illumina, Ion Torrent, and BGI/MGI (Beijing Genomics Institute/MGI Tech Co. Ltd.). Sequencing that requires longer reads can also use a third generation sequencing (3GS) technology such as ones from PacBio and Oxford Nanopore. In embodiments, a non-tumor, germline control sample from a subject with AML, such as a skin biopsy, can serve as a control to identify tumor-specific somatic mutations in the AML tumor.
[0063] Targeted gene panels use NGS to assess the mutational status of multiple genomic regions of interest simultaneously. The targeted panels include specific regions of the genome that are associated with a disease or phenotype of interest, for example, AML. Gene panels can assess point mutations, insertions and deletions, copy number variants (CNV), and translocations. The gene panels can be custom-designed or pre-designed. For example, a 76- gene panel for hematologic malignancies is available from OHSU (GeneTrails) for use in assessing samples from AML patients. Other gene panels for hematologic malignancies are available from Sequenom, Foundation Medicine (UTSW), Genoptix, and Illumina.
[0064] In FISH, a fluorescent dye-labeled nucleic acid (e.g., DNA) probe is hybridized to a full set of chromosomes from a genome, which are fixed to a glass microscope slide. FISH reveals the location of the labeled probe in the genome. Locus specific probes include a gene of interest (or fragment thereof) that allows visualization of which chromosome the gene is located on or how many copies of a gene exist within a particular genome. Centromeric repeat probes include repetitive sequences found at a chromosome centromere and can be used to assess whether an individual has the correct number of chromosomes. These probes can also be used in combination with "locus specific probes" to determine whether a subject is missing genetic material from a particular chromosome. Whole chromosome probes include collections of smaller probes, each of which binds to a different sequence along the length of a given chromosome, resulting in a full-color map of the chromosome known as a spectral karyotype. Whole chromosome probes are useful for examining chromosomal abnormalities, for example, translocations.
[0065] RNA-seq can help determine single nucleotide polymorphisms and gene fusion products. PCR and electrophoresis can be used to assess gene mutations on a gene-by-gene basis or on a limited number of genes.
(V) Drug families and drugs for AML.
[0066] Drugs have been developed to target genes known to be mutated in AML. The following table provides a summary of some drug families described in the present disclosure (see FIG. 7A) and exemplary drugs in each of these families.
Figure imgf000017_0001
Figure imgf000018_0001
Figure imgf000019_0001
[0067] In embodiments, FLT3 inhibitors can be administered to treat a subject with AML. FLT3 inhibitors include midostaurin, sunitinib, gilteritinib, lestaurtinib (CEP-701), crenolanib, quizartinib, and sorafenib.
[0068] In embodiments, IDH1 and/or IDH2 inhibitors can be administered to treat a subject with AML. IDH1 inhibitors include ivosidenib (AG-120) and olutasidenib. IDH2 inhibitors include enasidenib (AG-221 /CC-90007) and AGI-6780.
[0069] In embodiments, romidepsin alone or combined with other chemotherapy drugs can potentially cure or prevent resistance to chemotherapy caused by residual p53 mutation.
[0070] Responses to a number of drugs were found to have significant interaction with AML cell differentiation state and mutational status (see FIG. 5B). Vismodegib is a drug that functions as an antagonist of the smoothened receptor in the Hedgehog signaling pathway. It is approved for treatment of basal cell carcinoma. CHIR-99021 (IUPAC name: 6-((2-((4-(2,4-Dichlorophenyl)-5- (4-methyl-1 H-imidazol-2-yl)pyrimidin-2-yl)amino)ethyl)amino)nicotinonitrile) is a GSK-3 kinase inhibitor. Entospletinib is an inhibitor of spleen tyrosine kinase (Syk) in development for various types of cancer. Lapatinib is a synthetic, orally-active quinazoline that reversibly blocks phosphorylation of the epidermal growth factor receptor (EGFR), ErbB2, and the Erk-1 and-2 and AKT kinases. Lapatinib also inhibits cyclin D protein levels in human tumor cell lines and xenografts. Glesatinib (MGCD-265) is an orally bioavailable inhibitor of receptor tyrosine kinases, such as MET, VGFR1 -3, Tie, and Ron. Cabozantinib is a medication used to treat medullary thyroid cancer, renal cell carcinoma, and hepatocellular carcinoma. It inhibits the tyrosine kinases c-Met, VEGFR2, AXL, and RET. Barasertib (AZD1152-HQPA) is a selective inhibitor of Aurora B kinase. Canertinib (CI-1033) is an irreversible tyrosine kinase inhibitor effective against EGFR, HER-2, and ErbB-4. NVP-ADW742 is an orally active, selective inhibitor of insulin-like growth factor-1 receptor (IGF-1 R) and also inhibits c-kit kinase. MLN8054 is a selective inhibitor of Aurora A kinase.
[0071] Methods to treat subjects having AML and having gene mutations associated with AML are described in W02020/023921 .
(VI) AML cell differentiation states.
[0072] Six malignant cell types of AML along the HSC to myeloid axis have been identified previously by single cell RNA-seq and single cell genotyping: HSC-like, progenitor-like, granulocyte macrophage progenitor (GMP)-like, promonocyte-like, monocyte-like, and conventional dendritic cell (cDC)-like (van Galen etal., Cell 176:1265-1281 , 2019). In the present disclosure, it was found that AML cell differentiation state, along with mutational status, could predict whether a subject with AML would be sensitive or resistant to particular drugs (see, e.g., FIGs. 5A, 5B).
[0073] In embodiments, determining an AML cell differentiation state can include measuring expression of genes in a biological sample. Particular gene signatures were associated with the six malignant cell types (van Galen et al., Cell 176:1265-1281 , 2019).
[0074] In embodiments, an AML cell differentiation state is HSC-like when at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least sixteen, or at least seventeen, or at least eighteen, or at least nineteen, or at least twenty, or at least twenty-one, or at least twenty-two, or at least twenty-three, or at least twenty-four, or at least twenty-five, or at least twenty-six, or at least twenty-seven, or at least twenty-eight, or at least twenty-nine, or all of the following genes are expressed in a biological sample derived from an AML patient: NPTX2, H1 F0, EMP1 , MEIS1 , CALCRL, TPSD1 , TPT1 , CRHBP, CLNK, TSC22D1 , DST, NRIP1 , ABCB1 , GABRA4, ZBTB20, ABCA9, TPSB2, KMT2A, FAM30A, MEF2C, TMEM74, PDZRN4, ST3GAL1 , XIRP2, RBPMS, TMEM25, C20orf203, GNG1 1 , SLC6A13, and HOPX.
[0075] In embodiments, an AML cell differentiation state is progenitor-like, when at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least sixteen, or at least seventeen, or at least eighteen, or at least nineteen, or at least twenty, or at least twenty-one, or at least twenty-two, or at least twenty- three, or at least twenty-four, or at least twenty-five, or at least twenty-six, or at least twenty-seven, or at least twenty-eight, or at least twenty-nine, or all of the following genes are expressed in a biological sample derived from an AML patient: CDK6, HSP90AB1 , SPINK2, EEF1 B2, PCNP, TAPT1 -AS1 , HINT1 , LRRC75A-AS1 , DSE, PEBP1 , LOC107984974, H2AFY, EEF1 A1 , SMIM24, PSME1 , SOX4, LINC01623, EEF1 G, EBPL, EIF4B, PARP1 , MEST, TMEM70, TFDP2, ATP5G2, NAP1 L1 , MSI2, TPM4, SPN, and SELL
[0076] In embodiments, an AML cell differentiation state is granulocyte macrophage progenitor (GMP)-like, when at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least sixteen, or at least seventeen, or at least eighteen, or at least nineteen, or at least twenty, or at least twenty-one, or at least twenty-two, or at least twenty-three, or at least twenty-four, or at least twenty-five, or at least twenty-six, or at least twenty-seven, or at least twenty-eight, or at least twenty-nine, or all of the following genes are expressed in a biological sample derived from an AML patient: PRTN3, MPO, CALR, CLEC5A, ELANE, POU4F1 , TRH, TSPOAP1 , CEBPE, LINC01835, NUCB2, CSF3R, RUNX1T1 , CD38, PLPPR3, IGFBP2, PRRT4, SNHG5, FABP5, LOC100419170, CLEC1 1A, SERPINB1 , AZU1 , FBN2, HNRNPDL, HSPB1 , RNA5-8S, THSD7A, C12orf57, and FGFR1.
[0077] In embodiments, an AML cell differentiation state is promonocyte-like, when at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least sixteen, or at least seventeen, or at least eighteen, or at least nineteen, or at least twenty, or at least twenty-one, or at least twenty-two, or at least twenty- three, or at least twenty-four, or at least twenty-five, or at least twenty-six, or at least twenty-seven, or at least twenty-eight, or at least twenty-nine, or all of the following genes are expressed in a biological sample derived from an AML patient: DEFB1 , RNASE2, MS4A3, SERPINB10, SESN3, ZFR, MRPL33, CTSG, SLC44A1 , SLPI, FUT4, SRGN, CD70, PRLR, PLD3, LPL, RETN, TP53INP2, HSPA5, RNASE3, CCL23, EMB, ATP8B4, CLU, FAM107B, KBTBD11 , CSTA, ANKRD28, PIWIL4, and RNVU1 -6.
[0078] In embodiments, an AML cell differentiation state is monocyte-like, when at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least sixteen, or at least seventeen, or at least eighteen, or at least nineteen, or at least twenty, or at least twenty-one, or at least twenty-two, or at least twenty- three, or at least twenty-four, or at least twenty-five, or at least twenty-six, or at least twenty-seven, or at least twenty-eight, or at least twenty-nine, or all of the following genes are expressed in a biological sample derived from an AML patient: FCN1 , S100A12, MAFB, VCAN, S100A9, PLBD1 , SERPINA1 , BCL2A1 , THBS1 , PSAP, S100A8, FPR1 , C5AR1 , CD14, NAMPT, VNN2, CTSS, DUSP1 , CEBPB, CR1 , NFKBIA, SLC1 1A1 , LILRB3, BCL6, CYP1 B1 , TNFAIP2, MS4A10, AQP9, TLR4, and APOBEC3A.
[0079] In embodiments, an AML cell differentiation state is conventional dendritic cell (cDC)-like at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least sixteen, or at least seventeen, or at least eighteen, or at least nineteen, or at least twenty, or at least twenty-one, or at least twenty-two, or at least twenty-three, or at least twenty-four, or at least twenty-five, or at least twenty-six, or at least twenty-seven, or at least twenty-eight, or at least twenty-nine, or all of the following genes are expressed in a biological sample derived from an AML patient: MRC1 , HLA-DRB5, CST3, SAMHD1 , NAPSB, FCER1A, HLA-DRB1 , JAML, PKIB, HLA-DRA, HLA-DRB6, CPVL, HLA- DPB1 , HLA-DQA1 , HLA-DPA1 , CLEC4A, TMSB10, CAP1 , HLA-DQB1 , CRIP1 , CLEC10A, GPX1 , ITGB7, HLA-DQB2, DBI, FTH1 P3, ACTB, HLA-DQA2, SWOB, and ALDH2.
[0080] Methods are known to one of skill in the art for measuring gene expression, including RNA- seq, microarrays, and qRT-PCR.
[0081] In embodiments, determining an AML cell differentiation state can include analyzing AML cells microscopically and classifying an AML cell differentiation state by the French-American- British (FAB) system. For example, FAB subtypes M0-M2 had high HSC-like cell type scores, while FAB subtypes M4-M5 correlate with the monocyte-like cell type (see FIG. 3B).
[0082] In embodiments, determining an AML cell differentiation state can include detecting cell marker expression or absence by flow cytometry. In embodiments, HSC-like markers include: Lin-, CD11 b-, CD14-, CD15-, CD36-, CD38-, CD45RA-, CD64-, CD34+, CD49f+, and CD90+. In embodiments, progenitor-like markers include: Lin-, CD38-, CD45RA-, CD49f-, CD90-, CD34+, CD117+, CD123+, and CD302+. In embodiments, GMP-like markers include: Lin-, CD15+, CD16+, CD24+, CD34+, CD38+, and CD45RA+. In embodiments, promonocyte-like markers include: CD34-, CD1 1 b+, CD13+, CD14+, CD15+, CD36+, CD64+, and CD117+. In embodiments, monocyte-like markers include: Lin-, CD16-, CX3CR1 -, CD1 1 b+, CD14++, CD62L+, CD64+, CD68+, CD1 15+, CD163+, CD369+, CCR2 high, HLA-DR+, and VCAN+. In embodiments, conventional dendritic cell-like markers include: BDCA-1 +, CD8+, CD8alpha+, CD11 b+, CD11c+, CD103+, CD205+, CD206+, CD369+, HLA-DR+, and MHC Class II+.
(VII) Methods of Use.
[0083] The present disclosure provides for a method for determining the prognosis for a subject < 45 years old having AML, including: obtaining a biological sample derived from the subject; measuring in the biological sample a level of PEAR1 ; comparing the measured level of PEAR1 to a threshold level; and assigning to the subject a poor prognosis when the measured level of PEAR1 is greater than the threshold level. In embodiments, the method does not include measuring in the biological sample a level of pyrin domain (PYD) and caspase activation and recruitment domain (CARD) containing (PYCARD) gene or protein. In embodiments, the threshold level includes 0.329 PEAR1 RNA reads per kilobase of transcript per million total reads (RPKM). In embodiments, the biological sample includes peripheral blood or bone marrow aspirate. In embodiments, the subject has one or more mutations or gene rearrangements in TP53, RUNX1, ASXL 1, SRSF2, or GATA2-MECOM. In embodiments, the method further includes performing a stem cell transplant for the subject.
[0084] Poor prognosis for a subject < 45 years old having AML when the measured level of PEAR1 is greater than the threshold level can include: a decreased chance of overall survival, a decreased chance of relapse-free survival, a decreased chance of metastasis-free survival, a decrease in the time of survival (e.g., less than 10 years, less than 5 years, or less than one year), presence of a malignant tumor, an increase in the severity of disease, a decrease in response to therapy, an increase in tumor recurrence, an increase in metastasis, or the like. In embodiments, a poor prognosis includes a 75% survival probability or less at 500 days, or a 70% survival probability or less at 1000 days, or a 63% survival probability or less at 1500 days, or a 63% survival probability or less at 2000 days, or a 50% survival probability or less at 2500 days, or a 45% survival probability or less at 2500 days, from time of diagnosis or first treatment or remission. In embodiments, poor prognosis for a subject < 45 years old having AML when the measured level of PEAR1 is greater than the threshold level can take into consideration other prognostic factors described herein. [0085] With the information provided herein, methods are also enabled for using PEAR1 to diagnose a subject, for instance to diagnose a subject < 45 years of age as having AML. A subject who is thusly diagnosed with AML can further be selected for treatment, and/or treated for AML. Methods of treatment are known in the art, and are described herein.
[0086] The present disclosure provides for a method of treating a subject < 45 years old having AML, including administering a therapeutically effective amount of a drug that reduces or eliminates expression of PEAR1 or includes an antagonist of PEAR1 function. In embodiments, the drug includes a nucleic acid. For example, the nucleic acid can include an antisense RNA, a small interfering RNA, or a microRNA that disrupts expression of the PEAR1 gene. In embodiments, the drug includes an inhibitor of an integrin subunit. In embodiments, the drug inhibits allbp3 integrin. In embodiments, the drug includes eptifibatide. Eptifibatide is an antiplatelet drug that reversibly binds and inhibits glycoprotein llb/llla receptor of platelets. Eptifibatide is used to reduce ischemic cardiac events. In embodiments, the drug includes an antibody, or binding fragment thereof that binds to PEAR1 .
[0087] The present disclosure provides for a method of modifying a treatment regimen of a subject < 45 years old having AML, including: obtaining a biological sample derived from the subject; measuring in the biological sample a level of PEAR1 ; comparing the measured level of PEAR1 to a threshold level; and modifying the treatment regimen when the measured level of PEAR1 is greater than the threshold level. In embodiments, the method does not include measuring in the biological sample a level of pyrin domain (PYD) and caspase activation and recruitment domain (CARD) containing (PYCARD) gene or protein. In embodiments, the threshold level includes 0.329 PEAR1 RNA reads per kilobase of transcript per million total reads (RPKM). In embodiments, the biological sample includes peripheral blood or bone marrow aspirate. In embodiments, the modifying the treatment regimen includes enrolling the subject in a clinical trial testing a drug for treatment of AML. In embodiments, In embodiments, In embodiments, the modifying the treatment regimen includes stopping chemotherapy and providing a stem cell transplant for the subject. In embodiments, the modifying the treatment regimen includes changing from one chemotherapy drug or a combination of chemotherapy drugs to another chemotherapy drug or another combination of chemotherapy drugs. In embodiments, the subject has one or more mutations or gene rearrangements in TP53, RUNX1, ASXL1, SRSF2, or GATA2- MECOM.
[0088] The present disclosure provides for a method for identifying a subject having AML who is responsive to a drug, including: obtaining a biological sample derived from a subject having AML; determining a differentiation state of cells in the biological sample; determining mutational status of the subject; and identifying the subject as responsive to a drug. In embodiments, the subject is responsive to vismodegib (GDC-0449) when the differentiation state of cells is hematopoietic stem cell (HSC)-like and the subject has an MLLT3-KMT2A gene fusion. In embodiments, the subject is responsive to sorafenib when the differentiation state of cells is progenitor-like and the subject has an FL73-ITD mutation. In embodiments, the subject is responsive to CHIR-99021 when the differentiation state of cells is promonocyte-like and the subject has at least one mutation in KRAS. In embodiments, the subject is responsive to entospletinib (GS-9973), lapatinib, sunitinib, MGCD-265, or a combination thereof, when the differentiation state of cells is monocytelike and the subject has at least one mutation in NRAS. In embodiments, the subject is responsive to AGI-6780 when the differentiation state of cells is monocyte-like and the subject has at least one mutation in U2AF1.
[0089] The present disclosure provides for a method for identifying a subject having AML who is resistant to a drug, including: obtaining a biological sample derived from a subject having AML; determining a differentiation state of cells in the biological sample; determining mutational status of the subject; and identifying the subject as resistant to a drug. In embodiments, the subject is responsive to cabozantinib, MGCD-265, or sunitinib when the differentiation state of cells is promonocyte-like and the subject has an FL73-ITD mutation. In embodiments, the subject is responsive to MGCD-265, sunitinib, AZD1152-HQPA (AZD281 1 ), canertinib, sorafenib, cabozantinib, NVP-ADW742, or a combination thereof, when the differentiation state of cells is monocyte-like and the subject has an FL73-ITD mutation. In embodiments, the subject is responsive to MLN8054 when the differentiation state of cells is monocyte-like and the subject has at least one mutation in IDH2. In embodiments, the subject is responsive to CHIR-99021 when the differentiation state of cells is monocyte-like and the subject has at least one mutation in SRSF2.
[0090] The present disclosure provides for a method for identifying a subject having acute myeloid leukemia (AML) who is responsive to a drug, including: obtaining a biological sample derived from a subject having AML; determining mutational status of the subject; and identifying that the subject is responsive to a drug.
[0091] In embodiments, the subject is responsive to a cyclin-dependent kinase (CDK) inhibitor when the subject has at least one mutation in NRAS. In embodiments, the subject is responsive to a cyclin-dependent kinase (CDK) inhibitor when the subject has at least one mutation in KRAS. In embodiments, the CDK inhibitor comprises JNJ-7706621 , R547, roscovitine (CYC-202), flavopiridol, palbociclib, AST-487, AT7519, BMS-345541 , linifanib (ABT-869), or SNS-032 (BMS- 387032). [0092] In embodiments, the subject is responsive to an AGC kinase inhibitor when the subject has a CBFB-MYH11 gene fusion. In embodiments, an AGC kinase inhibitor comprises H-89, Go6976, LY-333531 , PP242, Midostaurin, BMS-345541 , AKT Inhibitor IV, GSK690693, MK-2206, or AKT Inhibitor X.
[0093] In embodiments, the subject is responsive to a phosphatidylinositol kinase inhibitor when the subject has at least one mutation in RUNX1. In embodiments, the subject is responsive to a phosphatidylinositol kinase inhibitor when the subject has at least one mutation in STAG2. In embodiments, the subject is responsive to a phosphatidylinositol kinase inhibitor when the subject has at least one mutation in TP53. In embodiments, a phosphatidylinositol kinase inhibitor comprises PI-103, TG100-115, BEZ235, GDC-0941 , LY294002, Idelalisib, or PP242.
[0094] In embodiments, the subject is responsive to a SRC kinase inhibitor when the subject has at least one mutation in IDH2. In embodiments, a SRC kinase inhibitor comprises bosutinib (SKI- 606), dasatinib, ibrutinib (PCI-32765), PD173955, ponatinib (AP24534), PP2, vandetanib (ZD6474), saracatinib (AZD0530), PLX-4720, KW-2449.
[0095] In embodiments, the subject is responsive to a JAK kinase inhibitor when the subject has at least one mutation in ASXL 1. In embodiments, a JAK kinase inhibitor comprises JAK Inhibitor I, JNJ-7706621 , ruxolitinib (INCB018424), tofacitinib (CP-690550), CYT387, PP242, TG101348, midostaurin, pelitinib (EKB-569), or BMS-345541.
[0096] In embodiments, the subject is responsive to a phosphatidylinositol-3 kinase-related kinase inhibitor when the subject has at least one mutation in TP53. In embodiments, a phosphatidylinositol-3 kinase-related kinase inhibitor comprises BEZ235, PI-103, PP242, Rapamycin, INK-128, or KU-55933.
[0097] Biomarkers and drug/mutational status/AML cell differentiation state associations described herein can be used to treat subjects (humans, veterinary animals (dogs, cats, reptiles, birds, etc.), livestock (horses, cattle, goats, pigs, chickens, etc.), and research animals (monkeys, rats, mice, fish, etc.)). Treating subjects includes providing therapeutically effective amounts. Therapeutically effective amounts include those that provide effective amounts, prophylactic treatments, and/or therapeutic treatments.
[0098] An “effective amount” is the amount of a composition necessary to result in a desired physiological change in a subject. Effective amounts are often administered for research purposes. Representative effective amounts disclosed herein can reduce symptoms associated with AML, improve overall survival, and/or promote complete remission. [0099] A "prophylactic treatment" includes a treatment administered to a subject who does not display signs or symptoms of a disease or nutritional deficiency or displays only early signs or symptoms of AML.
[0100] A "therapeutic treatment" includes a treatment administered to a subject who has AML.
[0101] Acute myeloid leukemia (AML) refers to a rapidly progressing cancer of the blood and bone marrow that affects a group of white blood cells (WBC) called myeloid cells. AML can also be referred to as acute myelogenous leukemia, acute myeloblastic leukemia, acute granulocytic leukemia and acute nonlymphocytic leukemia. A hematological malignancy refers to a cancer that affects the blood or bone marrow.
[0102] Symptoms of AML include: fever; weakness and fatigue; loss of weight and appetite; aches and pains in the bones or joints; tiny red spots in the skin; easy bruising and bleeding; frequent minor infections; and poor healing of minor cuts.
[0103] AML can be diagnosed using a karyotype analysis, which assesses the size, shape, and number of chromosomes in a biological sample. In embodiments, the biological sample includes a blood sample or bone marrow sample. Cytogenetic and gene mutations can also be detected by molecular methods, including whole genome sequencing, whole exome sequencing, gene panels for hematologic malignancies, PCR, and RNA sequencing.
[0104] In embodiments, AML can be characterized by morphology of the cancerous cells. AML can be classified by the type of normal, immature white blood cell it most closely resembles. The most common subtype is myeloid leukemia, where the cancer is in cells that normally produce neutrophils, phagocytes that make up 40% to 70% of WBC in humans. Another AML subtype includes monoblastic or monocytic leukemia. In monocytic leukemia, the cells look like monocytes, the largest WBC that can differentiate into macrophages and dendritic cells. In embodiments, leukemia cells can be a mixture of myeloblastic and monocytic cells. In embodiments, AML appear to originate from erythroid cells (that produce red blood cells) or platelets (megakaryocytic). Acute promyelocytic leukemia (APL) is a unique subtype of AML where the cancer cell stops maturing when the cell is at a stage called the promyelocyte or progranulocyte stage. APL is associated with a translocation between chromosomes 15 and 17 [t(15;17)].
[0105] A classification system for AML from the World Health Organization (WHO) includes these major groups of AML: AML with recurrent genetic abnormalities; AML with multilineage dysplasia (defined as the presence of 50% or more dysplastic cells in at least 2 cell lines); AML related to previous therapy (e.g., chemotherapy or radiation); AML, not otherwise categorized (NOS) (these include cases similar to the FAB classification, acute basophilic leukemia, and acute panmyelosis with fibrosis); myeloid sarcoma (also known as granulocytic sarcoma or chroroma); myeloid proliferations related to Down Syndrome; and undifferentiated and biphenotypic acute leukemias (leukemias with both lymphocytic and myeloid features).
[0106] AML subtypes may also be classified by the French-American-British (FAB) classification system, which is based on morphology and cytochemistry (Bennett et al., Ann Intern Med. 103(4):620-625, 1985). The subtypes include: MO, myeloblastic without differentiation; M1 , myeloblastic with little or no maturation; M2, myeloblastic with maturation; M3, promyelocytic (e.g., APL); M4, myelomonocytic; M4eo, myelomonocytic with eosinophils; M5a, monocytic without differentiation (monoblastic); M5b, monocytic with differentiation; M6, erythroid leukemia; and M7, megakaryoblastic leukemia. In embodiments, the MO to M5 subtypes start in immature forms of WBC. In embodiments, the M6 subtype starts in immature forms of red blood cells. In embodiments, the M7 subtype starts in immature forms of cells that make platelets.
[0107] In embodiments, AML can be classified by the cytogenetic, or chromosome, changes found in leukemia cells. In embodiments, particular chromosomal changes are closely matched with the morphology of the AML cells. Chromosomal changes are commonly grouped according to the likelihood that treatment will work against the subtype of AML.
[0108] Types of cytogenetic changes found in AML include: a translocation, where a chromosome breaks off and reattaches to another chromosome; an inversion, where a single chromosome undergoes breakage and rearrangement within itself; extra copies of a chromosome; and a deletion of a part or all of a chromosome.
[0109] Chemotherapy is the primary treatment for AML. Chemotherapy is the use of drugs to destroy cancer cells. In embodiments, chemotherapy inhibits or reduces cancer cells’ ability to grow and divide. Systemic chemotherapy is delivered through the bloodstream to reach cancer cells throughout the body. Chemotherapy can be administered: by an intravenous (IV) tube placed into a vein using a needle; by injection into the cerebral spinal fluid (CSF); in a pill or capsule that is swallowed (orally); and/or by a subcutaneous injection. In embodiments, when chemotherapy is administered into a larger vein, a central venous catheter or port can be placed in the body.
[0110] A chemotherapy regimen, or schedule, usually includes a specific number of cycles given over a set period of time. In embodiments, a patient receives one drug at a time. In embodiments, a patient receives combinations of 2 or more different drugs given at the same time. Several drugs are used to treat AML, which are discussed herein.
[0111] Chemotherapy for AML can be divided into 3 phases: induction, post-remission, and consolidation. Induction therapy is the first period of treatment after a subject’s diagnosis. The goal of induction therapy is a complete remission (CR). In embodiments, a subject has a CR when: blood counts have returned to normal; leukemia cannot be found in a bone marrow sample when examined under the microscope; and/or there are no longer any signs and symptoms of AML. In embodiments, induction therapy can include two rounds of chemotherapy.
[0112] In embodiments, a combination of cytarabine (Cytosar-U) and an anthracycline drug (e.g., daunorubicin (Cerubidine) or idarubicin (Idamycin)) is administered to a subject with AML during induction therapy. In embodiments, cytarabine is administered over 4 to 7 days and daunorubicin or idarubicin is administered for 3 days. In embodiments, a 7+3’ regimen is followed in induction therapy, where cytarabine is infused continuously for 7 days and then an anthracycline is administered for 3 days.
[0113] In embodiments, hydroxyurea (Droxia, Hydrea) is administered to a subject with AML during induction therapy. In embodiments, hydroxyurea helps lower WBC counts. In embodiments, WBC counts can be reduced temporarily by leukapheresis. In leukapheresis, the patient’s blood is passed through a special machine that removes WBC (including leukemia cells) and returns the rest of the blood to the patient. Intravenous lines or a single catheter is used for the leukapheresis.
[0114] In embodiments, hypomethylating agents decitabine (Dacogen) and/or azacytidine (Vidaza), and/or low dose cytarabine is administered to older adults.
[0115] In embodiments, other chemotherapy drugs that are used include: cladribine (2-CdA), fludarabine, mitoxantrone, etoposide (VP-16), 6-thioguanine (6-TG), corticosteroid drugs (e.g., prednisone or dexamethasone), methotrexate (MTX), and 6-mercaptopurine (6-MP). In embodiments, patients with poor heart function do not tolerate anthracyclines, so they are treated with another chemotherapy drug, such as fludarabine (Fludara) or etoposide.
[0116] Post-remission therapy includes administering a variety of different drugs to destroy AML cells that remain but cannot be detected by medical tests. AML will almost certainly recur if no further treatment is given after a CR. In embodiments, a subject undergoes a bone marrow/stem cell transplantation as part of post-remission therapy.
[0117] Consolidation therapy is continued treatment provided to keep AML from recurring. Consolidation therapy includes chemotherapy and stem cell transplantation. In embodiments, younger adults in remission are administered 2 to 4 rounds of high- or intermediate-dose cytarabine or other intensive chemotherapy at monthly intervals. In embodiments, the following chemotherapy drugs can be used in consolidation therapy: amsacrine, high dose cytarabine, etoposide, daunorubicin, fludarabine, and idarubicin.
[0118] In embodiments, each chemotherapy drug is administered to a subject with AML at a therapeutically effective dose. In embodiments, cytarabine is administered at 100 mg/m2/d by continuous IV infusion on days 1-7 for induction therapy. In embodiments, cytarabine is administered at 100 mg/m2 IV every 12 hours on days 1-7 for induction therapy. In embodiments, cytarabine is administered at 3 g/m2 IV over 1 -3 hours every 12 hours for four doses for relapsed ALL.
[0119] In embodiments, daunorubicin is administered at 30-45 mg/m2/d IV for 3 days in combination therapy for remission induction therapy. In embodiments, total cumulative dose for daunorubicin is 550 mg/m2. In embodiments, a liposomal preparation of daunorubicin is administered at 40 mg/m2 IV every 2 weeks.
[0120] In embodiments, cladribine is administered at 0.09 mg/kg/d (4 mg/m2/d) by continuous IV infusion for 7 consecutive days.
[0121] In embodiments, fludarabine is administered at 25 mg/m2/d IV over 30 min for 5 days. In embodiments, the administration is repeated every 28 days.
[0122] In embodiments, hydroxyurea is administered intermittently at 80 mg/kg PO every third day. In embodiments, hydroxyurea is administered continuously at 20-30 mg/kg PO daily.
[0123] In embodiments, idarubicin is administered at 12 mg/m2/d IV for 3 days every 3 weeks in combination therapy.
[0124] In embodiments, methotrexate is administered at a low dose of 2.5-5.0 mg PO daily, or 5-25 mg/m2 PO, IM, IV twice weekly, or 50 mg/m2 IV every 2-3 weeks. In embodiments, methotrexate is administered at a high dose of 1-12 g/m2 IV with leucovorin rescue every 1-3 weeks. In embodiments, methotrexate is administered intrathecally at 5-10 mg/m2 (up to 15 mg) every 3-7 days.
[0125] In embodiments, mitoxantrone is administered at 12 mg/m2/d IV for 3 days, in combination with cytarabine for remission induction therapy.
[0126] In embodiments, younger patients in whom cytogenetic or molecular studies predict a poorer prognosis with only chemotherapy undergo a bone marrow/stem cell transplantation as consolidation therapy.
[0127] Because chemotherapy also targets dividing cells in healthy tissue, side effects of chemotherapy include losing hair, developing mouth sores, nausea, and vomiting. In embodiments, subjects with AML undergoing chemotherapy receive drugs to alleviate side effects of chemotherapy. Chemotherapy lowers the body’s ability to fight infection, which can lead to increased bruising, bleeding, and/or fatigue. In embodiments, subjects with AML undergoing chemotherapy receive antibiotics to prevent and treat infections and/or receive transfusions of red blood cells and platelets. [0128] In embodiments, for patients whose leukemia cells express the CD33 protein, the targeted drug gemtuzumab ozogamicin (Mylotarg) can be added to chemotherapy treatment.
[0129] AML can be treated with a stem cell transplant or a bone marrow transplant. The medical procedure involves destroying cancer cells in the marrow, blood, and other parts of the body using high doses of chemotherapy and/or radiation therapy and then introducing replacement blood stem cells, called hematopoietic stem cells (HSC), to create healthy bone marrow. HSC are blood- forming cells found both in the bloodstream and in the bone marrow. In embodiments, the procedure is a stem cell transplant when stem cells in the blood are transplanted. In embodiments, the procedure is a bone marrow transplant when bone marrow tissue is transplanted. The stem cell transplantation can be allogeneic (stem cells originating from a donor) or autologous (stem cells originating from the subject with AML). In embodiments, allogeneic stem cell transplants are used for AML.
[0130] AML can be treated with targeted therapy. Targeted therapy targets the leukemia’s specific genes, proteins, or the tissue environment that contributes to the growth and survival of the leukemia. In embodiments, targeted therapy blocks the growth and spread of leukemia cells while limiting damage to healthy cells. In embodiments, a subject who has relapsed or refractory AML with an IDH2 mutation is administered enasidenib (Idhifa). In embodiments, a subject who has relapsed or refractory AML with a FLT3 gene mutation is administered gilteritinib (Xospata). In embodiments, a subject who has relapsed or refractory AML with an IDH1 gene mutation is administered ivosidenib (Tibsovo). In embodiments, a subject who has AML with a FLT3 gene mutation is administered midostaurin (Rydapt). Refractory AML occurs when leukemia is still present after initial treatment.
[0131] In embodiments, a subject with the APL subtype of AML is administered a combination of an all-trans retinoic acid (ATRA) and arsenic trioxide (Trisenox). In embodiments, ATRA is administered orally. In embodiments, a subject with the APL subtype who receives a combination of ATRA and arsenic trioxide are very likely to have a CR. In embodiments, a subject with the APL subtype of AML can receive chemotherapy containing regimens with idarubicin, daunorubicin, and/or cytarabine. In embodiments, arsenic trioxide can be used alone during induction therapy. In embodiments, arsenic trioxide can be used in combination with ATRA during post-remission therapy or if APL comes back after treatment.
[0132] AML can be treated with radiation therapy. Radiation therapy is the use of high-energy x- rays or other particles to destroy cancer cells. In embodiments, radiation therapy includes external-beam radiation therapy, which is radiation given from a machine outside the body. In embodiments, a radiation therapy regimen, or schedule, includes a specific number of treatments given over a set period of time. In embodiments, radiation therapy is used when leukemia cells have spread to the brain or to shrink a myeloid sarcoma. Side effects from radiation therapy may include fatigue, mild skin reactions, upset stomach, and loose bowel movements.
[0133] Palliative or supportive care can be undertaken to relieve a subject’s symptoms and side effects due to treatment of AML. Palliative or supportive care includes supporting the patient with their physical, emotional, and social needs. Palliative treatments can include medication, nutritional changes, relaxation techniques, emotional support, and other therapies.
[0134] In embodiments, a subject with AML is in remission or having “no evidence of disease” (NED) when the leukemia cannot be detected in the body, there are no symptoms, and/or a patient’s blood counts are normal. In embodiments, a subject with AML is in remission when a bone marrow biopsy shows few bone marrow cells (hypocellular bone marrow) and only a small portion of blasts (making up no more than 5% of the bone marrow). A remission may be temporary or permanent. If the leukemia does return after the original treatment, it is called recurrent or relapsed leukemia. In embodiments, treatment for recurrent or relapsed AML can include the treatments described herein, such as chemotherapy, stem cell transplantation, targeted therapy, and radiation therapy, but they may be used in a different combination or given at a different pace. [0135] The treatment for recurrent AML often depends on the length of the initial remission. If the AML comes back after a long remission, the original treatment can work again. If the remission was short, then other drugs can be used, often through a clinical trial. An allogeneic stem cell transplant may be the best option for patients whose leukemia has come back after initial treatment. However, many drugs and other approaches are being researched in clinical trials and these may provide other treatment options.
[0136] Recovery from leukemia is not always possible. If the leukemia cannot be cured or controlled, the disease may be called advanced or terminal.
[0137] A number of factors are known to contribute to the outlook for a subject with AML or a subject’s risk of AML recurring after treatment, including AML subtype, age, genetic changes, cytogenetic changes, WBC count, response to chemotherapy, presence or absence of minimal residual disease, whether the AML is therapy-related, time of relapse, history of another blood disorder, uncontrolled infection at the time of diagnosis of AML, and spread of AML to the central nervous system.
[0138] In embodiments, chromosomal (cytogenetic) changes can contribute to prognosis. In embodiments, chromosomal changes are grouped as follows: 1 ) favorable (associated with more successful treatment): translocation or inversion of chromosome 16, a translocation between chromosomes 8 and 21 , and a translocation between chromosomes 15 and 17; 2) intermediate: chromosomal changes associated with a less favorable prognosis include normal chromosomes, where no changes are found and a translocation between chromosomes 9 and 1 1 [t(9;1 1 )]; 3) unfavorable: chromosomal changes that are associated with less successful treatment or with a low chance of curing the AML include extra copies of chromosomes 8 or 13 [for example, trisomy 8 (+8)], deletion of all or part of chromosomes 5 or 7, changes to chromosome 3 at band q26, translocation between chromosomes 6 and 9, translocation between chromosomes 9 and 22, abnormalities of chromosome 1 1 , and complex changes on many chromosomes. In embodiments, extra copies of chromosome 8 or trisomy 8 may be classified as intermediate risk or unfavorable. Human chromosomes are numbered from 1 to 22. Human sex chromosomes are called “X” or “Y.” The letters “p” and “q” refer to the “arms” or specific areas of the chromosome. [0139] In embodiments, age can contribute to prognosis. In embodiments, younger adults (e.g., < 60 years of age) have a more favorable prognosis than older adults. In embodiments, chromosomal abnormalities can happen as a person gets older. In embodiments, older people can have other health conditions that make it difficult to cope with side effects of treatments for AML.
[0140] In embodiments, gene mutations can contribute to prognosis. Testing for molecular changes at diagnosis helps determine a patient’s treatment options. Up to 50% of people with AML have a mutation in the nucleophosmin (NPM1) gene. In embodiments, the NPM1 gene mutation is linked with a more favorable prognosis if there are no other abnormalities. 30% of people with AML have an internal tandem duplication, FLT3-ITD, in the FMS-like tyrosine kinase 3 (FLT3) gene. In embodiments, changes to the CEBPA gene are linked to a more favorable prognosis. In embodiments, FLT3-ITD is linked with a less favorable prognosis. In embodiments, patients with changes in the NPM1 or CEBPA genes have a better long-term outcome, while chemotherapy does not work as well for patients with changes in the FLT3 gene. In embodiments, overexpression of the EPG gene in people with AML points to a less favorable prognosis.
[0141] Other genetic changes linked to prognosis for people with AML include: RUNX1, ASXL1, TP53, IDH1, and IDH2.
[0142] In embodiments, WBC count can contribute to prognosis. In embodiments, a WBC count of more than 100,000 at the time of diagnosis is linked with a less favorable prognosis for AML. [0143] In embodiments, favorable changes occur more commonly in subjects < 60 years of age with AML, while unfavorable changes are more common in people > 60 years of age with AML. In embodiments, treatment is successful in the long term for 50% to 60% of subjects younger than 60 with AML that is classified as favorable and for less than 10% of patients < 60 years of age with AML that is classified as unfavorable. Prognosis in patients older than 60 years of age is significantly worse.
[0144] In embodiments, response to chemotherapy can contribute to prognosis. People who reach complete remission after induction chemotherapy have a more favorable prognosis than those who have refractory disease that does not respond to treatment. The response to chemotherapy is measured as the time it takes to reach a complete remission, or complete response. In embodiments, the prognosis is more favorable when a complete remission is reached within 4 weeks of starting chemotherapy. In embodiments, the prognosis is less favorable when it takes > 4 weeks to reach complete remission. In embodiments, the prognosis is poorer in subjects with AML who don’t reach a complete remission after chemotherapy.
[0145] In embodiments, a subject with AML who has minimal residual disease (MRD) any time after the start of consolidation therapy (the continued treatment given to keep leukemia from coming back) has a higher risk of relapse and a poorer prognosis. MRD refers to the presence of leukemia cells, or blasts, in the bone marrow, that can only be detected by very sensitive tests, such as flow cytometry or polymerase chain reaction (PCR) and not by standard tests such as microscopy.
[0146] In embodiments, a subject with AML who has early relapse has poorer (i.e. less favorable) prognosis. An early relapse means that the leukemia returns soon after treatment. Recurrent or relapsed AML is cancer that has come back after treatment.
[0147] In embodiments, a subject who has AML that develops after treatment for another cancer has poorer (i.e. less favorable) prognosis.
[0148] In embodiments, a subject with AML who has or already had another blood disorder, such as a myelodysplastic syndrome (MDS), has a poorer (i.e. less favorable) prognosis.
[0149] In embodiments, a subject who has a serious, uncontrolled infection at the time of diagnosis of AML has poorer (i.e. less favorable) prognosis.
[0150] In embodiments, spread of AML to the brain and spinal cord (i.e. the central nervous system, or CNS) is a poor prognostic factor.
[0151] Therapeutic treatments can be distinguished from effective amounts based on the presence or absence of a research component to the administration. As will be understood by one of ordinary skill in the art, however, in human clinical trials effective amounts, prophylactic treatments and therapeutic treatments can overlap.
[0152] For administration, therapeutically effective amounts (also referred to herein as doses) can be initially estimated based on results from in vitro assays and/or animal model studies. Such information can be used to more accurately determine useful doses in subjects of interest. [0153] The actual dose amount administered to a particular subject can be determined by the subject, a physician, veterinarian, or researcher taking into account parameters such as physical, physiological and psychological factors including target, body weight, condition, previous or concurrent therapeutic interventions, and/or idiopathy of the subject.
[0154] Therapeutically effective amounts can be achieved by administering a dose including 0.0001 pg/kg body weight to 10 mg/kg body weight per dose, or 0.0001 pg/kg body weight to 0.001 pg/kg body weight per dose, or 0.001 pg/kg body weight to 0.01 pg/kg body weight per dose, or 0.01 pg/kg body weight to 0.1 pg/kg body weight per dose, or 0.1 pg/kg body weight to 10 pg/kg body weight per dose, or 1 pg/kg body weight to 100 pg/kg body weight per dose, or 100 pg/kg body weight to 500 pg/kg body weight per dose, or 500 pg/kg body weight per dose to 1000 pg/kg body weight per dose, or 1 .0 mg/kg body weight to 10 mg/kg body weight per dose.
[0155] Therapeutically effective amounts can be achieved by administering single or multiple doses during the course of a treatment regimen (e.g., hourly, every 2 hours, every 3 hours, every 4 hours, every 6 hours, every 9 hours, every 12 hours, every 18 hours, daily, every other day, every 3 days, every 4 days, every 5 days, every 6 days, weekly, every 2 weeks, every 3 weeks, or monthly).
[0156] One or more drug(s) can be administered simultaneously or within a selected time window, such as within 10 minutes, 1 hour, 3 hour, 10 hour, 15 hour, 24 hour, or 48 hour time windows or when the complementary active agent(s) is within a clinically-relevant therapeutic window.
[0157] Treatment can include administering a drug by any appropriate route including: orally; parenterally including buccally, sublingually, sublabially, mucosal; by inhalation; intra-arterially; intravenously; intraventricularly; intramuscularly; subcutaneously; transdermally; intraspinally; intraorbitally; intracranially; and intrathecally.
[0158] The Exemplary Embodiments and Example(s) below are included to demonstrate particular embodiments of the disclosure. Those of ordinary skill in the art should recognize in light of the present disclosure that many changes can be made to the specific embodiments disclosed herein and still obtain a like or similar result without departing from the spirit and scope of the disclosure.
(VIII) Exemplary Embodiments.
[0159] 1 . A method for determining the prognosis for a subject < 45 years old having acute myeloid leukemia (AML), including: obtaining a biological sample derived from the subject; measuring in the biological sample a level of platelet endothelial aggregation receptor 1 (PEAR1 ); comparing the measured level of PEAR1 to a threshold level; and assigning to the subject a poor prognosis when the measured level of PEAR1 is greater than the threshold level.
[0160] 2. The method of embodiment 1 , wherein the method does not include measuring in the biological sample a level of pyrin domain (PYD) and caspase activation and recruitment domain (CARD) containing (PYCARD) gene or protein.
[0161] 3. The method of embodiment 1 or 2, wherein the threshold level includes 0.329 PEAR1 RNA reads per kilobase of transcript per million total reads (RPKM).
[0162] 4. The method of any of embodiments 1 -3, wherein the biological sample includes peripheral blood or bone marrow aspirate.
[0163] 5. The method of any of embodiments 1 -4, wherein the subject has one or more mutations or gene rearrangements in TP53, RUNX1 , ASXL1 , SRSF2, or GATA2-MECOM.
[0164] 6. The method of any of embodiments 1 -5, wherein the method further includes performing a stem cell transplant for the subject.
[0165] 7. A method of treating a subject < 45 years old having acute myeloid leukemia (AML), including administering a therapeutically effective amount of a drug that reduces or eliminates expression of PEAR1 or includes an antagonist of PEAR1 function.
[0166] 8. The method of embodiment 7, wherein the drug includes a nucleic acid.
[0167] 9. The method of embodiment 7 or 8, wherein the drug includes an inhibitor of an integrin subunit.
[0168] 10. The method of any of embodiments 7-9, wherein the drug includes eptifibatide.
[0169] 1 1. The method of embodiment 7 or 8, wherein the drug includes an antibody, or binding fragment thereof that binds to PEAR1 .
[0170] 12. A method for identifying a subject having acute myeloid leukemia (AML) who is responsive to a drug, including: obtaining a biological sample derived from a subject having AML; determining a differentiation state of cells in the biological sample; determining mutational status of the subject; and identifying the subject as responsive to a drug when (a) the differentiation state of cells is hematopoietic stem cell (HSC)-like and the subject has an MLLT3-KMT2A gene fusion;
(b) the differentiation state of cells is progenitor-like and the subject has an FLT3-ITD mutation;
(c) the differentiation state of cells is promonocyte-like and the subject has at least one mutation in KRAS; (d) the differentiation state of cells is monocyte-like and the subject has at least one mutation in NRAS; and/or (e) the differentiation state of cells is monocyte-like and the subject has at least one mutation in U2AF1 .
[0171] 13. A method of diagnosing a subject as having drug responsive acute myeloid leukemia (AML), comprising: obtaining a biological sample derived from a subject having AML; determining a differentiation state of cells in the biological sample; determining mutational status of the subject; and identifying the subject as responsive to a drug when (a) the differentiation state of cells is hematopoietic stem cell (HSC)-like and the subject has an MLLT3-KMT2A gene fusion;
(b) the differentiation state of cells is progenitor-like and the subject has an FLT3-ITD mutation;
(c) the differentiation state of cells is promonocyte-like and the subject has at least one mutation in KRAS; (d) the differentiation state of cells is monocyte-like and the subject has at least one mutation in NRAS; and/or (e) the differentiation state of cells is monocyte-like and the subject has at least one mutation in U2AF1 .
[0172] 14. The method of embodiment 12 or embodiment 13, wherein: for (a) the drug includes vismodegib (GDC-0449); for (b) the drug includes sorafenib; for (c) the drug includes CHIR-99021 ; for (d) the drug includes entospletinib (GS-9973), lapatinib, sunitinib, or MGCD-265; and for (e) the drug includes AGI-6780.
[0173] 15. A method for identifying a subject having acute myeloid leukemia (AML) who is resistant to a drug, including: obtaining a biological sample derived from a subject having AML; determining a differentiation state of cells in the biological sample; determining mutational status of the subject; and identifying the subject as resistant to a drug when (a) the differentiation state of cells is promonocyte-like and the subject has an FLT3-ITD mutation; (b) the differentiation state of cells is monocyte-like and the subject has an FLT3-ITD mutation; (c) the differentiation state of cells is monocyte-like and the subject has at least one mutation in IDH2; and/or (d) the differentiation state of cells is monocyte-like and the subject has at least one mutation in SRSF2. [0174] 16. A method of diagnosing a subject as having drug resistant acute myeloid leukemia (AML), comprising: obtaining a biological sample derived from a subject having AML; determining a differentiation state of cells in the biological sample; determining mutational status of the subject; and identifying the subject as resistant to a drug when (a) the differentiation state of cells is promonocyte-like and the subject has an FLT3-ITD mutation; (b) the differentiation state of cells is monocyte-like and the subject has an FLT3-ITD mutation; (c) the differentiation state of cells is monocyte-like and the subject has at least one mutation in IDH2; and/or (d) the differentiation state of cells is monocyte-like and the subject has at least one mutation in SRSF2.
[0175] 17. The method of embodiment 15 or embodiment 16, wherein: for (a) the drug includes cabozantinib, MGCD-265, or sunitinib; for (b) the drug includes MGCD-265, sunitinib, AZD1152-HQPA (AZD2811 ), canertinib, sorafenib, cabozantinib, or NVP-ADW742; for (c) the drug includes MLN8054; and for (d) the drug includes CHIR-99021 .
[0176] 18. A method for identifying a subject having acute myeloid leukemia (AML) who is responsive to a drug, including: obtaining a biological sample derived from a subject having AML; determining mutational status of the subject; and identifying that the subject is responsive to a drug when: (a) the subject has at least one mutation in NRAS, and wherein the drug includes a cyclin-dependent kinase (CDK) inhibitor; (b) the subject has at least one mutation in KRAS, and wherein the drug includes a cyclin-dependent kinase (CDK) inhibitor; (c) the subject has a CBFB- MYH11 gene fusion, and wherein the drug includes a AGC kinase inhibitor; (d) the subject has at least one mutation in RUNX1 , and wherein the drug includes a phosphatidylinositol kinase inhibitor; (e) the subject has at least one mutation in STAG2, and wherein the drug includes a phosphatidylinositol kinase inhibitor; (f) the subject has at least one mutation in TP53, and wherein the drug includes a phosphatidylinositol kinase inhibitor; (g) the subject has at least one mutation in IDH2, and wherein the drug includes a SRC kinase inhibitor; (h) the subject has at least one mutation in ASXL1 , and wherein the drug includes a JAK kinase inhibitor; and/or (i) the subject has at least one mutation in TP53, and wherein the drug includes a phosphatidylinositol-3 kinase- related kinase inhibitor.
[0177] 19. A method for identifying a subject having acute myeloid leukemia (AML) who is responsive to a drug, comprising: obtaining a biological sample derived from a subject having AML; determining mutational status of the subject; and identifying that the subject is responsive to a drug when (a) the subject has at least one mutation in NRAS, and wherein the drug comprises a cyclin-dependent kinase (CDK) inhibitor; (b) the subject has at least one mutation in KRAS, and wherein the drug comprises a cyclin-dependent kinase (CDK) inhibitor; (c) the subject has a CBFB-MYH1 1 gene fusion, and wherein the drug comprises a AGC kinase inhibitor; (d) the subject has at least one mutation in RUNX1 , and wherein the drug comprises a phosphatidylinositol kinase inhibitor; (e) the subject has at least one mutation in STAG2, and wherein the drug comprises a phosphatidylinositol kinase inhibitor; (f) the subject has at least one mutation in TP53, and wherein the drug comprises a phosphatidylinositol kinase inhibitor; (g) the subject has at least one mutation in IDH2, and wherein the drug comprises a SRC kinase inhibitor; (h) the subject has at least one mutation in ASXL1 , and wherein the drug comprises a JAK kinase inhibitor; and/or (i) the subject has at least one mutation in TP53, and wherein the drug comprises a phosphatidylinositol-3 kinase-related kinase inhibitor.
[0178] 20. The method of embodiment 18 or claim 19, wherein: for (a) and (b) the drug includes JNJ-7706621 , R547, roscovitine (CYC-202), flavopiridol, palbociclib, AST-487, AT7519, BMS-345541 , linifanib (ABT-869), or SNS-032 (BMS-387032); for (c) the drug includes H-89, Go6976, LY-333531 , PP242, Midostaurin, BMS-345541 , AKT Inhibitor IV, GSK690693, MK-2206, or AKT Inhibitor X; for (d) to (f) the drug includes PI-103, TG100-115, BEZ235, GDC-0941 , LY294002, Idelalisib, or PP242; for (g) the drug includes bosutinib (SKI-606), dasatinib, ibrutinib (PCI-32765), PD173955, ponatinib (AP24534), PP2, vandetanib (ZD6474), saracatinib (AZD0530), PLX-4720, KW-2449; for (h) the drug includes JAK Inhibitor I, JNJ-7706621 , ruxolitinib (INCB018424), tofacitinib (CP-690550), CYT387, PP242, TG101348, midostaurin, pelitinib (EKB-569), or BMS-345541 ; and for (i) the drug includes BEZ235, PI-103, PP242, Rapamycin, INK-128, or KU-55933.
[0179] 21. A method of modifying a treatment regimen of a subject < 45 years old having acute myeloid leukemia (AML), including: obtaining a biological sample derived from the subject; measuring in the biological sample a level of platelet endothelial aggregation receptor 1 (PEAR1 ); comparing the measured level of PEAR1 to a threshold level; and modifying the treatment regimen when the measured level of PEAR1 is greater than the threshold level.
[0180] 22. The method of embodiment 21 , wherein the method does not include measuring in the biological sample a level of pyrin domain (PYD) and caspase activation and recruitment domain (CARD) containing (PYCARD) gene or protein.
[0181] 23. The method of embodiment 21 or embodiment 22, wherein the threshold level includes 0.329 PEAR1 RNA reads per kilobase of transcript per million total reads (RPKM).
[0182] 24. The method of any of embodiments 21 -23, wherein the biological sample includes peripheral blood or bone marrow aspirate.
[0183] 25. The method of any of embodiments 21 -24, wherein the modifying the treatment regimen includes enrolling the subject in a clinical trial testing a drug for treatment of AML.
[0184] 26. The method of any of embodiments 21 -25, wherein the modifying the treatment regimen includes stopping chemotherapy and providing a stem cell transplant for the subject.
[0185] 27. The method of any of embodiments 21 -25, wherein the modifying the treatment regimen includes changing from one chemotherapy drug or a combination of chemotherapy drugs to another chemotherapy drug or another combination of chemotherapy drugs.
[0186] 28. The method of any of embodiments 21 -25, wherein the subject has one or more mutations or gene rearrangements in TP53, RUNX1 , ASXL1 , SRSF2, or GATA2-MECOM.
(IX) Experimental Examples.
[0187] Example 1. Integrative analysis of drug response and clinical outcome in acute myeloid leukemia.
[0188] This Example describes the identification of a single gene, platelet endothelial aggregation receptor 1 (PEAR1 ), that can be used as a biomarker to predict outcome for an AML patient < 45 years of age. Integrative analysis of data including genomic, transcriptomic, cell differentiation states, and clinical annotations also reveal broad association of drug sensitivity with tumor cell differentiation state, new predictors of drug sensitivity based on mutational status and/or cell-type, and additional associations between drug response, cell-type, and mutational status based on organization of drugs into gene and pathway target families. At least some of these data described in this Example was published as Bottomly et al. (Cancer Cell 40:850-865, August 8, 2022; referred to herein as Bottomly et al., 2022).
[0189] To better understand the complexity of AML genetics and cell biology, and the manner by which these diverse factors govern drug response and clinical outcome, a comprehensive platform to combine clinical, cellular, and molecular features of disease was developed. The complete OHSU Beat AML cohort represents sample collection and characterization over a span of 10 years with integration of ex vivo drug sensitivity testing, curation of clinical annotations, and DNA- and RNA-sequencing to reveal mutational status and gene expression profiles. The data in Tyner etal. (Nature 562, 526-531 , 2018) include the first two waves of patient accrual and sample data from 11 academic medical centers (denoted as “Waves 1 +2”). In this Example, additional longitudinal samples for Waves 1 +2, updated clinical information, as well as new patient accrual, which represents the final two waves (“Waves 3+4”) were provided. Waves 3+4 include a total of 293 patient specimens from 279 patients (243 patients unique to Waves 3+4). Also provided were harmonization of these data sets together, for a cumulative cohort of 942 specimens from 805 patients, which reflected a real-world cohort of AML cases, inclusive of de novo, transformed, and therapy-related AML as well as cases at the point of initial diagnosis (70% of cases) and smaller number of cases with relapsed or residual disease. All somatic variant calls, gene expression counts, and drug response data can be explored and visualized through the interactive browser, Vizome (vizome.org). For all cohort-level analyses, only specimens from the first timepoint of each patient were used, with all remission samples excluded. A broad overview of clinical data showed comparable features between the two datasets with only a slightly lower percentage of cases with de novo AML in the first dataset (49% in Waves 1 +2 vs. 58% in Waves 3+4). Frequencies of the most commonly mutated genes were also equivalent between cohorts (see Figure 1 A of Bottomly et al., 2022) with some minor variances. Clinical outcomes, as measured by overall survival, of patients whose specimens were collected during the first cohort were indistinguishable from those collected during the second cohort (see Figure 1 B of Bottomly et al., 2022). The ex vivo drug sensitivity data were compared in de novo samples for each dataset, due to prior observation that general drug sensitivity is reduced in secondary AML cases (Tyner et al., Nature 562, 526-531 , 2018). Comparing drug sensitivity in de novo cases in each cohort showed extremely high concordance (see Figure 1 C of Bottomly et al., 2022). Further with regard to Figures 1A-1 D of Bottomly et al., 2022, genomic features and outcomes for the Beat AML cohorts are highly concordant. The somatic mutation percentages are highly similar (average difference is 1.321%) between the cohorts despite differences in sequencing capture platform (shown in Figure 1 A of Bottomly et al., 2022). Except for a larger number of exceptional survivors in Waves 1 +2, the cumulative survival curves are highly overlapping (p-value 0.2 log-rank test) (shown in Figure 1 B of Bottomly etal., 2022). Within de novo AML patients, the average AUC values from ex vivo drug response (points) show highly correlated Pearson’s correlation (r=0.965) (shown in Figure 1 C of Bottomly etal., 2022). Waves 3+4 can serve as validation cohort to assess prior mutation-inhibitor associations (Tyner et al., Nature 562, 526-531 , 2018) (shown in Figure 1 D of Bottomly et al., 2022). The difference in average response of specimens that are mutated versus wild type for a given gene (effect size) are plotted for Waves 1 +2 on the x-axis and Waves 3+4 on the y-axis. Effect sizes were expressed as Glass’s delta with respect to the wild type group. Significance was based on the Welch’s t-test comparing mutated vs wild type and requiring a minimum of five mutations in Waves 1 +2 and three mutations in Waves 3+4. Adjusted significance in Waves 1 +2, Waves 3+4, or both are also annotated (q value < 0.05; (Storey et al., Proc Natl Acad Sci U S A 100, 9440-9445, 2003).
[0190] The RNA-Seq data were harmonized across the waves to maintain consistency with the Waves 1 +2 results (see Figure 2A of Bottomly et al., 2022), by re-using the parameters and reference distribution of the conditional quantile normalization (CQN; Hansen et al., Biostatistics 13, 204-216, 2012) procedure applied previously. Prior weighted gene co-expression network analysis (WGCNA; Zhang & Horvath, Stat Appl Genet Mol Biol 4, Articlel 7, 2005) identified 13 gene expression modules across Waves 1 +2 (Tyner et al., Nature 562, 526-531 , 2018). Part of the WGCNA methodology involved the summarization of each module by its first principal component (PC) score (termed an eigengene; Horvath & Dong, PLoS computational biology 4, e10001 17, 2008). Using the parameters (e.g. means, standard deviations and rotations) learned from Waves 1 +2, the PC scores of the Waves 3+4 samples for each module were predicted. Examination of the first two PCs of these 13 WGCNA modules revealed near complete overlap in terms of range, distributions, and clustering of the two datasets in every module (see Figure 2B of Bottomly etal., 2022), highlighting the effectiveness of the harmonization strategy. Finally, prior analysis included integration of ex vivo drug sensitivity with both mutational/cytogenetic status as well as with gene expression to identify genomic or transcriptomic features of disease that correspond with drug sensitivity or resistance. Strong validation of associations between drug sensitivity and mutational status was observed, with nearly all associations showing a similar effect size in both the positive and negative direction and numerous associations that passed stringent statistical thresholds in both datasets (see Figure 1 D of Bottomly et al., 2022). In addition, correlation between the 13 WGCNA module eigengenes and drug response was assessed, revealing high concordance for Waves 1 +2 vs. Waves 3+4 (see Figure 2C of Bottomly et al., 2022). Further with regard to Figures S1A-S1 C of Bottomly et al., 2022, these figures show normalized Expression by Cohort. To harmonize the data Waves, the reference distribution and parameters learned from the conditional quantile normalization (CQN) performed on the Waves 1 +2 cohort were used and applied to Waves 3+4 samples. The resulting values were comparable as judged by boxplots of the distribution of normalized gene expression (Y-axis) vs patient sample (X-axis) (shown in Figure S1 A of Bottomly et al., 2022). The boxplots are colored as presented in Figure 1 A of Bottomly et a!., 2022. The first two principal components of weighted gene coexpression network analysis (WGCNA) expression profiles generated in Waves 3+4 are highly overlapping with respect to range and clustering relative to Waves 1 +2, suggesting feasibility of re-use of WGCNA modules (shown in Figure S1 B of Bottomly et a!., 2022). Agreement of the correlation between drug response and WGCNA module eigengene values between cohorts. For both cohorts the Pearson’s correlation was computed between each WGCNA module eigengene and the AUC of each inhibitor (shown in Figure S1 C of Bottomly et al., 2022). At least 30 patient samples were required for each module/inhibitor combination. The points are colored by the average correlation between waves with respect to the sensitive (red, lower left quadrant) to resistance (blue, top right quadrant) scale defined in Bottomly et al., 2022. The top three sensitive and resistant relationships are called out in text.
[0191] Deconvoluting AML Cell Maturation State. It has been observed that drug response patterns in AML are sometimes correlated with maturation state of AML tumor cells, with certain drugs exhibiting stronger efficacy against tumors with less differentiated cell state (e.g. BCL2i, CDK4/6i; (Kuusanmaki et al., Haematologica 105, 708-720, 2020; Majumder et al., Haematologica, 2019; Pei et al., Cancer Discov O, 536-551 , 2020; Romine et al., Blood Cancer Discov 2, 518-531 , 2021 ; Zhang et al., Nat Cancer 1 , 826-839, 2020)) and others showing better efficacy against tumors with more differentiated state (e.g. BETi, MEKi; (Romine et aL, 2021 ; White, B.S. et al., NPJ Precis Oncol 5, 71 , 2021 )). Analytical approaches can facilitate the deconvolution of deep sequencing data to infer proportions of distinct cell types (Avila Cobos et a , Bioinformatics 34, 1969-1979, 2018), and single-cell sequencing technology has recently defined gene expression patterns for six distinct cell-types in AML (Hematopoietic Stem Cell (HSC)-like, Progenitor-like, Granulocyte-Monocyte Progenitor (GMP)-like, Promonocyte-like, Monocyte-like, conventional Dendritic Cell (cDC)-like); van Galen et al., Cell 176, 1265-1281 e1224, 2019). Each of these six signatures were summarized by their PC1 scores (similar to the WGCNA eigengene). An example of the formation of the Monocyte-like score is shown in Figure S2A of Bottomly et al., 2022. Comparison of the least differentiated HSC-like score or one of the most differentiated Monocyte-like scores against FAB subtypes shows higher overall scores for HSC-like in the less differentiated M0-M2 samples and higher Monocyte-like scores in the myelomonocytic and monocytic M4-M5 groups (as shown in Figure S2B of Bottomly et al., 2022). Differentiation scoring correlates with clinical annotation of tumor maturation state. Malignant celltype scores for each patient were generated relative to 6 sets of 30 genes derived from expression signatures from single-cell sequencing (van Galen et a!., Cell 176, 1265-1281 e1224, 2019). Based off of the eigengene methodology of WGCNA, cell-type scores were computed as the first principal components (top barplot) of each gene set (heatmap) where rows are genes and columns are the samples from Waves 1 +2 (shown in Figure S2A of Bottomly et al., 2022). The scores for Waves 3+4 were then computed. Comparing the combined set of scores with French- American-British (FAB) blast morphology classification, it was observed that the HSC-like and Monocyte-like scores are inversely related across the FAB classifications (M3 being an exception). As expected, the highest scores in the HSC-like category are in the lower FAB categories (MO-2) whereas the highest Monocyte-like scores are in the more differentiated M4-5 categories (as shown in Figure S2B of Bottomly et al., 2022). To understand the relationship between the WGCNA modules and differences in cell-type, eigengene scores of each of these signatures were compared. WGCNA modules that correlate strongly with the more differentiated cell-types (N=5), less differentiated cell-types (N=3), as well as those that exhibit some correlation with early- and late-stage cell-types (N=5) (see Figure 2A of Bottomly et al., 2022) were found. Finally, the study investigated whether mutational or cytogenetic features showed enrichment for AML cell-type signatures. Indeed, numerous significant correlations, including TP53, BCOR, and SF3B1 enriched for HSC-like state, NPM1 enriched for Promonocyte-like, RAS correlating with Monocyte-like, RUNX1 showing correlation with both HSC-like and cDC-like, and numerous other associations were observed (see Figure 2B of Bottomly etal., 2022). Differentiation scoring allows characterization of distinct expression and mutational patterns. Malignant cell-type scores for each patient were generated relative to 6 sets of 30 genes derived from expression signatures from single-cell sequencing (van Galen et al., Cell 176, 1265-1281 e1224, 2019). By examining the correlation of expression module eigengenes and the cell-type scores the WGCNA expression modules (X-axis) were separated by differentiation cell-type profiles (y-axis). Pearson’s correlation R-values are annotated (as shown in Figure 2A of Bottomly etal., 2022). Similarly, the mutational events significantly associated with increased cell-type score were determined. Shown in Figure 2B of Bottomly et al., 2022 are the signed -log (Welch’s t-test p-values) for the differences in cell-type score with respect to mutational status. Up to the top 5 most significant (q value < 0.05; Storey et al., Proc Natl Acad Sci U S A 100, 9440-9445, 2003) events associated with increased cell-type score are highlighted.
[0192] Analyses were conducted to determine the breadth of drugs with response that is correlated with AML cell differentiation state and, accordingly, compared the six cell-type scores with drug response across the full panel. Hierarchical clustering revealed three main clusters (based on Pearson’s correlation between cell-type score and drug response, where a strong negative correlation indicates an association between cell phenotype and drug sensitivity). One cluster contained a series of inhibitors with greater activity against HSC- and Progenitor-like states and with less efficacy against the more differentiated cell-types. Another cluster displayed the inverse pattern with greatest activity against the eDC- and Monocyte-like states and resistance associated with HSC- and Progenitor-like. The third cluster showed some, albeit muted, activity against both HSC-/Progenitor-like and cDC-/Monocyte-like states with the most resistance conferred by GMP- and Promonocyte-like states (see Figure 3A of Bottomly et al., 2022). Influence of cell-type on inhibitor response. Correlating inhibitor AUC with the six cell-type scores allows inhibitors (x-axis) to be divided into three main groups based on the relationship between differentiation and resistance (blue) or sensitivity (red) (as shown in Figure 3A of Bottomly et al., 2022). Note, only those drugs with a significant main effect (BY FDR < 0.05; Benjamini & Yekutieli, The Annals of Statistics 29, 1165-1188, 2001 ) are shown in the heatmap. The two inhibitors that cluster distinctly from all others, Panobinostat and Venetoclax, are shown correlated with Monocyte-like score in Figure S3A of Bottomly et al., 2022. Asterisks indicate those inhibitors in which there is an interaction between mutation and cell-type, shown in FIG. 1 herein (single asterisk, q value < 0.1 ; double asterisk, q value < 0.05; Storey et al., Proc Natl Acad Sci U S A 100, 9440-9445, 2003). Finally, there were two drugs, venetoclax and the histone deacetylase (HDAC) inhibitor, panobinostat, that were extreme outliers and exhibited inverse patterns of response (see Figure S3A of Bottomly etal., 2022). Since numerous drug response patterns have also been seen to significantly correlate with mutational or cytogenetic events, as shown in FIG. 1 D, it was next investigated whether any of the associations between drug response and cell-type score might be conditional on mutational or cytogenetic events. Numerous instances of significant interactions between cell-type score and mutational/cytogenetic events were identified, mostly involving the Monocyte-like score (FIG. 1 ). For example, sorafenib is a potent inhibitor of the FLT3 receptor tyrosine kinase and exhibits significantly greater efficacy in FLT3-ITD mutant samples. However, inclusion of cell-type scores reveals that sorafenib shows stronger efficacy in cases with a higher Progenitor-like score and the existence of a prominent Monocyte-like signal confers resistance of FLT3-ITD-positive AML cases to sorafenib (see Figure S3B of Bottomly etal., 2022). Cell-type inhibitor correlations are illustrated in Figures S3A and S3B of Bottomly etal., 2022. The two outlier inhibitors from Figure 3A of Bottomly et al., 2022, Panobinostat and Venetoclax, are shown to have an opposing linear relationship with the Monocyte-like score (see Figure S3A of Bottomly etal., 2022). The color scheme follows the high (yellow) vs low (purple) gradient for celltype scoring. Sorafenib sensitivity shows no discernible association with cell-type scores in the FLT3-ITD negative subgroup (see Figure S3B of Bottomly et al., 2022). However, in the FLT3- ITD positive subgroup opposing linear associations with Monocyte-like and Progenitor-like scores can be seen (statistical analysis provided in FIG. 1 herein).
[0193] Drug Family Responses. Drug response data were organized into groups of inhibitors based on shared targets or pathways. Drug-target relationship data from the Cancer Targetome (Blucher et al., Trends in Pharmacological Sciences 38, 1085-1099, 2017; Blucher et al., F1000 Research 8, 908, 2019; Choonoo etal., PLoS One 14, e0223639, 2019) and other sources (Davis et al., Nat Biotechnol 29, 1046-1051 , 201 1 ) were aggregated to yield 25 drug families with some overlap of drugs between families based on known polypharmacology. Variable overall response of drug families across all samples tested was observed, with some families (e.g. Phosphatidyl inositol 3' kinase-related kinases (PIKK)) showing greater overall activity and others (e.g. Aurora Kinase (AurK)) showing generally less sensitivity (FIG. 2A herein). By examining response of each drug family in specimens with wild type versus mutated status, numerous instances where mutational events exhibit significantly greater sensitivity or resistance to drug family classes were identified (FIG. 2B herein). In addition to expected associations such as FLT3- ITD with Type III receptor tyrosine kinase (RTK) inhibitors and RAS mutations with STE7 (MEK) inhibitors, numerous unexpected associations were identified, including TP53 mutation showing greater sensitivity to phosphatidylinositol kinase family (PIK) inhibitors and N/KRAS mutation conferring greater sensitivity to cyclin-dependent kinase inhibitors. Correlations between drug family activity and tumor cell-type scores were also assessed and strong correlations were found with 18 of the 25 drug families with one or more tumor cell-type signature (see Figure 5A of Bottomly et al., 2022). Drug family response is influenced by and conditional on cell-type. Similar to the single-inhibitor analyses shown in Figures 3A and 3B of Bottomly et al., 2022, the drug families can be grouped by their association with cell-type differentiation scores based on their Pearson’s correlation (BY FDR < 0.05; Benjamini & Yekutieli, The Annals of Statistics 29, 1165- 1 188, 2001 ) Finally, a number of drug family correlations with cell-type scores were also observed that were conditional on mutational/cytogenetic events, such as for PIK family inhibitors where high HSC-like scores correlated with resistance and strong cDC-like scores correlated with sensitivity in NPM1 mutated cases (FIG. 3 herein). [0194] Determinants of Clinical Outcome. Finally, correlations between the WGCNA expression modules and cell-type scores with clinical variables were explored within the dataset, including overall survival, tumor burden and cell composition, age, transformed or therapy-related status, and prognostic categories. To ensure robust associations with overall survival, this analysis was limited to samples collected at initial acute leukemia diagnosis with RNASeq data (n=435). Clustering of these features revealed one branch that contained three WGCNA modules (3, 9, and 12) as well the HSC-like cell-type score which associated with shorter overall survival (see Figure 6A of Bottomly etal., 2022). Of these, modules 3 and 9 showed the strongest associations with survival. The genes in module 3 (n=203) and module 9 (n=119) were prioritized by computing the correlation with the eigengene (termed kME; (Fuller etal. Mamm Genome 18, 463-472, 2007)) to quantify each gene’s degree of hub status. This revealed a single gene, platelet endothelial aggregation receptor 1 (PEAR1 ), that correlated far more strongly with the module 3 eigengene than did any other module gene member (see Figure 6B of Bottomly et al., 2022). No similar singular driver gene was identified for module 9. Additionally, both module 3 and PEAR1 expression were positively correlated with HSC-like score (see Figure 6B of Bottomly etal., 2022). Conditional inference forest methodology (cforest; Hothorn et al., J Computational and Graphical Statistics 15, 651 -674, 2006) was next applied to understand the relative importance of patient age, WGCNA module eigengenes, cell-type scores, and mutational/cytogenetic status on overall survival. For this analysis, a further restriction to only evaluate cases of de novo AML (n=298) was placed because of the observed association between AML type and other variables. Validating known risk factors, this methodology identified age and mutated TP53 (Papaemmanuil et al., N Engl J Med 374, 2209-2221 , 2016) in addition to WGCNA module 3 as the strongest predictors of poor outcome. For young (<45 years) and 7P53-wild type middle (45-60 years) age group patients, the analysis revealed module 3 expression to be the strongest determinant of outcome (see Figure 6C of Bottomly et al., 2022). Comprehensive analysis of expression signatures and clinical variables identifies PEAR1 expression as a prognostic factor in AML is shown in Figures 6A-6C of Bottomly et al., 2022. The 6 cell-type scores and the set of 9 non- redundant WGCNA eigengenes were analyzed for univariate association with categorical and continuous clinical variables as well as overall survival across and within age subsets. Each variable type was grouped on the Y-axis and the displayed association values were derived from the corresponding test statistics (as shown in Figure 6A of Bottomly et al., 2022). These include the Z statistic for categorical (logistic regression) and survival outcomes (Cox Proportional Hazards) and T-statistic for the continuous outcomes (general linear model). Average linkage hierarchical clustering was applied to rows (within groups) and columns (as showing in Figure 6B of Bottomly et al., 2022; top) Of all the 203 genes in module 3, PEAR1 has the highest correlation with the module eigengene (kME). (as shown in Figure 6B of Bottomly et al., 2022; bottom) The patient expression values (dots) of PEAR1 (x-axis) correlated highly with the Mod3 module eigengene (y-axis) and both are also correlated with the ‘HSC-like’ malignant cell-type as indicated by the cell-type purple(low)-yellow(high) gradient as defined in FIG. 3A. A strategy based on a forest of conditional inference trees (cforest) was used to determine subgroups differentiating overall survival from a combination of age groups, expression modules (denoted with Mod*), gene mutation, AML gene fusions and cell-type scores data in the harmonized dataset. The age groups are young (<45), middle (45-60), older (60-75), oldest (>75). Depiction of the resulting surrogate ctree with variables indicated as ovals with indicated significance for the optimal split (see Figure 6C of Bottomly et al., 2022). Lines indicate values that were split on, for instance, “TRUE” for the ‘young’ variable, which indicates that the subgroup is <45 years old. The resulting subgroups of patients, rectangles that denote ‘terminal nodes’, are listed with the subgroup size (denoted as n) and are colored to match corresponding survival curves.
[0195] Association of individual PEAR1 gene with clinical parameters was next explored, since PEAR1 was so strongly associated with the overall module 3 score. Assessment of PEAR1 expression as it relates to European LeukemiaNet (ELN) prognostic categories indicated a significant difference in PEAR1 expression between Adverse and Favorable risk categories in patients 60 and older (3.765 mean increase in Adverse; P value=3.276e-25; N=172). PEAR1 expression, however, showed a smaller but significant difference in the middle age group (1 .497 mean increase; P value=0.006; N=72) and was not significantly associated with Adverse vs. Favorable ELN risk in young patients (P value=0.170; N=79) despite its strong association with outcome in this group (FIG. 4A herein). PEAR1 expression was compared to the leukemic stem cell 17-gene signature (LSC17) (Ng et al., Nature 540, 433-437, 2016) in this young subset of patients and it was observed that expression of PEAR1 could distinguish outcomes in young patients to a similar degree as the LSC17 signature (FIG. 4B herein). To further compare PEAR1 with LSC17, the hazard ratios for PEAR1 and LSC17 were assessed in all age groups and in the young subset. Additionally, cases where the specimen obtained was a bone marrow aspirate were further stratified, due to improved splitting of outcomes observed with bone marrow compared with peripheral blood specimens (see Figures S4A-S4D of Bottomly et al., 2022). Prognostic association of PEAR1 is dependent on originating tissue type. Considering the entire cohort of Denovo patient RNASeq samples with survival information and limiting to peripheral blood or leukapheresis samples, a significant split of PEAR1 using a conditional inference tree (ctree) was not found (as shown in Figure S4A of Bottomly et al., 2022). However, using bone- marrow derived samples allowed differentiation of prognostic groups (as shown in Figure S4B of Bottomly et al., 2022). Focusing on the young cohort (<45 years), prognostic groups are again only able to be formed using a ctree approach in the bone-marrow derived samples (as shown in Figures S4C and S4D of Bottomly et al., 2022); significance was determined using the Log Rank test. The hazard ratios revealed similar performance of PEAR1 with LSC17 (FIG. 4C herein), noting predictive range is influenced by subset and split type (see Methods). Finally, since PEAR1 showed strong upregulation in transformed AML, which is known to exhibit inferior outcome (Figures 6A, S5A, and S4B of Bottomly etal., 2022), and since a similar association was observed with LSC17 and transformed AML (P value=2.18e-07), PEAR1 and LSC17 in all cases with specimens obtained at initial acute leukemia diagnosis, inclusive of transformed and therapy- related AML cases, were compared. Mutational and clinical association with PEAR1 expression are shown in Figures S5A-S5C of Bottomly et al., 2022. Patients that had a prior MPN, MDS, or MDS/MPN diagnosis (Transformed) were seen to have significantly higher PEAR1 expression (Welch’s T-test P value < 1 .5e-05) than those that had AML diagnoses without a known originating factor (Denovo). Expression values for both cohorts were evenly represented in the diagnosis groups (shown in Figure S5A of Bottomly et al., 2022). This significant increase in PEAR1 expression for Transformed patients relative to Denovo was seen across the range of the Monocyte-like cell-type scores, however a significant interaction was observed with respect to the HSC-like score (P value=0.024) (shown in Figure S5B of Bottomly et al., 2022). For each AML mutation and fusion, the difference in average PEAR1 expression between mutated and wild type (shown as points with size given by the number of mutated samples) was computed . This difference is reported in terms of a standardized effect size (Glass’s delta relative to wild type; x- axis) (shown in Figure S5C of Bottomly etal., 2022). The vertical dashed line indicates mutational association with increased expression on the right, and with decreased expression on the left. Unadjusted significance of these differences is given on the y-axis with a dashed line indicating the 0.05 level. Blue genes indicate those that are significant (BY FDR < 0.05; Benjamini & Yekutieli, The Annals of Statistics 29, 1165-1 188, 2001 ), red (circles marked with x’s) otherwise. This analysis revealed PEAR1 to perform equivalently to LSC17 both in the young subset and across all age groups (FIGs. 5A-5D herein).
[0196] Finally, to validate the prognostic significance of PEAR1 using independent patient cohorts, the capacity of PEAR1 expression to stratify overall survival was examined using a dataset from Malani and coworkers as well as from The Cancer Genome Atlas (TCGA) (Cancer Genome Atlas Research, N Engl J Med 368, 2059-2074, 2013 Malani et al., Cancer Discov. 12:388-401 , 2022). It was found that elevated PEAR1 expression conferred shortened overall survival in a strongly significant manner in both datasets (Figures S7A-S7D of Bottomly et al., 2022).
[0197] A recent study examined the AML Cancer Genome Atlas dataset (Cancer Genome Atlas Research, N Engl J Med 368, 2059-2074, 2013) to identify an immune prognostic signature for poor prognostic subsets of AML. By performing differential expression analysis for cases likely to exhibit immune suppression due to poor prognostic genetic features, followed by enrichment for immune-related gene sets, an immune prognostic gene expression model was developed for AML, which included PEAR1 as well as another gene, PYCARD (Dao et al., Scientific Reports 11 , 4856, 2021 ). This immune prognostic model showed correlation with T-cell scores deconvoluted from TCGA transcriptomic data as well as expression of certain immune checkpoint molecules. It was also shown to correlate with poor clinical outcome in several AML datasets; however, a role for PEAR1 in shaping leukemic cell biology or cell-type was not considered.
[0198] This Example shows that PEAR1 expression predicts outcome in young AML patients independently of ELN category, performs equivalently to the LSC17 gene signature, and correlates strongly with an HSC-like signature (association of PYCARD with outcome was not found; PYCARD by itself was not seen as significant in either the overall de novo AML cohort (P value = 0.929; Univariate Cox PH) or the young age group (P value = 0.637; Univariate Cox PH)). Increased PEAR1 was confirmed in cases with mutation of TP53, RUNX1, and ASXL1 (as shown in Dao et al., Scientific Reports 1 1 , 4856, 2021 ), and elevation in other genetic subsets with poor prognostic markers (SRSF2 mutation, GA7A2-MECOM) and lower expression with certain good prognostic features (NPM1, CEBPA) were also observed (Figure S5C of Bottomly et al., 2022). Finally, while there was an increasing trend in expression of initial diagnosis PEAR1 across ethnicities (Non-Hispanic white: mean 1.395, N=359; African Heritage: mean 2.205, N=13), the smaller sample size in the non-Caucasian groups warrants further investigation in a validation cohort. This is potentially important due to recent findings of outcome disparities among Black AML patients (Bhatnagar et al., Cancer Discov 11 , 626-637, 2021 ) as well as several studies reporting racially-skewed representation of PEAR1 polymorphisms that impact on PEAR1 expression/function (Herrera-Galeano et al., Arterioscler Thromb Vase Biol 28, 1484-1490, 2008; Keramati, A.R. et al., Platelets 30, 380-386, 2019; Qayyum et al., BMC Genet 16, 58, 2015).
[0199] Elevation of PEAR1 expression in ASXL /-mutated AML is notable, since ASXL1 is one of the most commonly mutated genes in clonal hematopoiesis of indeterminate potential (CHIP) and individuals with CHIP exhibit significantly elevated cardiovascular events (Genovese etal., N Engl J Med 371 , 2477-2487, 2014; Jaiswal et al., N Engl J Med 371 , 2488-2498, 2014). PEAR1 genetics and function have been tied to the biology of cardiovascular events, raising the intriguing possibility that these findings could be connected. Platelet levels have been shown to decline with age (Biino et al., PLoS One 8, e54289, 2013), and a leukocyte pool with enhanced platelet aggregation potential - possibly facilitated by increased PEAR1 downstream of somatic mutational events such as ASXL 1 - could represent a selective advantage within an aging hematopoietic microenvironment.
[0200] All of these data raise the possibility of PEAR1 as a new therapeutic target in heme malignancies. As noted herein, PEAR1 phosphorylation can be blocked with an inhibitor of the integrin subunits that facilitate PEAR1 activation downstream of platelet aggregation (eptifibatide). PEAR1 also appears to be an active signaling molecule in this context, suggesting that smallmolecule approaches could mitigate PEAR1 signaling as a potential therapeutic avenue. Antibodies to PEAR1 have also been developed, so a large-molecule approach to PEAR1 targeting may also be feasible. Indeed, we have performed preliminary validation of this concept by showing PEAR1 surface expression on tumor cells from AML patient samples (Figure S7C of Bottomly et al., 2022) with the degree of surface expression strongly correlated with PEAR1 transcript levels (Figure S7D of Bottomly etal., 2022). Interruption of normal PEAR1 function must be considered; however, platelet function was not compromised in a PEAR1 -null mouse (Criel et al., Thromb Res 146, 76-83, 2016).
[0201] Collectively, this Example exemplifies the utility of integrative analysis that incorporates functional testing of large cohorts of primary patient specimens. Through this approach, most observations have been validated in two independently collected datasets, and the harmonized dataset as well as cutting-edge analytical methods have been leveraged to extract many new findings. These include broad association of drug sensitivity with tumor cell differentiation state, new predictors of drug sensitivity based on mutational status, sometimes conditional on cell-type, and additional associations between drug response, cell-type, and mutational status based on organization of drugs into gene and pathway target families. Given the strong associations that were seen between cell-type score and drug response, the distribution of these cell phenotypes should be assessed when evaluating clinical differences in drug sensitivity. Testing interactions between cell-type and other disease or patient features can help identify potential confounders or modifiers of drug response. For key comparisons, cell-type consideration should be done as routine as population substructure adjustment. Finally, the analysis of clinical outcome has yielded a novel prognostic and potentially targetable feature of AML. Example 2. Material and Methods.
Experimental Model and Subject Details.
[0202] Patient Sample Collection and Cohort Organization. The complete OHSU Beat AML cohort represents sample collection and characterization over a span of 10 years. The initial cohort, first reported in (Tyner et al., Nature 562, 526-531 , 2018) represent the first two waves of patient accrual and sample data (denoted “Waves 1 +2"). Additional longitudinal samples for Waves 1 +2, in addition to new patient accrual represents the final two waves (“Waves 3+4"). Harmonization of these datasets together for specific analyses was denoted as the “harmonized data”. A specimens from the first available timepoint (defined by a 5-day interval) for each patient was utilized such that each patient was represented by a single sample for a given datatype. Additionally, samples taken while a patient was in remission were removed from consideration.
[0203] All patients gave informed consent to participate in this study, which had the approval and guidance of the institutional review boards at Oregon Health & Science University (OHSU), University of Utah, University of Texas Medical Center (UT Southwestern), University of Miami, University of Colorado, University of Florida, National Institutes of Health (NIH), and University of Kansas (KUMC). Samples were sent to the coordinating center (OHSU; IRB#9570; #4422; NCT01728402) where they were coded and processed. Specific names of centers associated with each specimen were coded and centers providing less than 5 samples were aggregated together and given one center identifier. Clinical, prognostic, genetic, cytogenetic, and pathologic lab values as well as treatment and outcome data were manually curated from patient electronic medical records. Patients were assigned a specific diagnosis based on the prioritization of genetic and clinical factors as determined by WHO guidelines (Arber et al., Blood 127, 2391 -2405, 2016). To prevent re-identification, any patient over the age of 90 was placed into a >90 aggregated age bracket. Genetic characterization of the leukemia samples included results of clinical deepsequencing panels of genes commonly mutated in hematologic malignancies (Sequenom and GeneTrails (OHSU); Foundation Medicine (UTSW); Genoptix; and Illumina).
Method Details.
[0204] Patient Specimen Processing. Processing of patient specimens, isolation of nucleic acids, DNA- and RNA-sequencing, detection of FLT3-ITD and 4-base pair NPM1 insertion, performance of ex vivo drug sensitivity assays, and initial analytical workflows were performed in identical fashion as in a prior study (Tyner etal., Nature 562, 526-531 , 2018). Any changes that were made to analytical approaches and new techniques employed in Example 1 are described below. [0205] DNA Sequencing. For DNA sequencing, the 1 1.9 megabase custom capture library that was developed to provide coverage of all variants previously reported in AML was used (including all new variants that were detected from exome sequencing in the prior study, Tyner etal., Nature 562, 526-531 , 2018). The genes, variants, and capture regions for this custom library were reported in the Supplementary Information of the prior study (Table S14 in Tyner et al., Nature 562, 526-531 , 2018), as well as detailed methods for pre-processing and analysis (Supplemental Methods).
[0206] European Leukemia Network (ELN) Prognostic Categorization. Some updates were implemented to the pipeline for calling ELN categories. First, calls in this study were confined to specimens taken at initial acute leukemia diagnosis, and did not place specimens that were taken in remission, at relapse, or from cases with a non-AML diagnosis (e.g. MDS) into ELN categories. Second, for consideration of mutation of ASXL 1, RUNX1, or TP53, internal deep sequencing data were used as the primary source of information, rather than clinical sequencing results curated from the electronic medical record. This was due to a higher percentage of samples with available sequencing data for these three genes from the internal versus curated clinical data. Internal results were compared with available clinical sequencing results for these genes, and the minority of instances where discrepancies existed were manually adjudicated. Calls for bi-allelic CEBPA were also manually adjudicated, using both internal sequencing and results from clinical sequencing. Relevant cytogenetic events were parsed from clinical karyotype and cytogenetic results, and consensus FLT3-ITD and NPM1 calls were used as described in the prior study (Tyner et al., Nature 562, 526-531 , 2018). All of these results were used to call Favorable, Intermediate, or Adverse categories according to the ELN 2017 criteria established in (Dohner et al., Blood 129, 424-447, 2017) with a minority of cases requiring indication of uncertainty due to lack of knowledge regarding FLT3-ITD allelic ratio. Finally, usage of calls based on the 2008 ELN guidelines were discontinued.
[0207] Ex vivo Functional Drug Screen Data Processing. Dose response curves for all the drugs were fit using probit models (Kurtz et al., Proc Natl Acad Sci U S A 1 14, E7554-E7563, 2017) similar to the flagship Beat AML manuscript (Tyner et al., Nature 562, 526-531 , 2018). However, the QC and summarization approach was modified for the situations where multiple replicates were available. In the description below, a profile indicates a sample - inhibitor pair. Profiles with no replicates were subject to probit curve fit thresholds. Specifically, profiles with both Deviance > 2 and AIC > 12 were removed. Profiles with replicates were first assessed based on the variability of the readouts (normalized viability capped at 100%). For each plate, the standard deviation (SD) across replicates for each of the 7 concentration points was assessed and the plate was removed if 4 or more of the concentrations were considered highly variable (SD > 25). If replicates were available across multiple plates, the viability at each concentration point was first averaged per plate. The SD across plates was computed per concentration point similar to above and an AUC was computed from probit fits to each plate. Similar to the initial replicate filter, profiles were removed if both 4 or more concentrations had highly variable SD and the SD(AUC) > 50. Finally, a single probit curve was used to determine the AUC and IC values for each profile. As with Tyner et al. (Nature 562, 526-531 , 2018), drug response is indicated by color or shading coding scheme (i.e., Red - sensitivity, white - intermediate, Blue - resistance).
[0208] Organization of Drugs into Families Based on Targets and Pathways. Two primary data sources were utilized for information regarding drug/target relationships, Targetome (Blucher et al., Trends in pharmacological sciences 38, 1085-1099, 2017; Blucher et al., F1000 Research 8, 908, 2019; Choonoo et al., PLoS One 14, e0223639, 2019) and KINOMEscan (Davis et al., Nat Biotechnol 29, 1046-1051 , 2011 ). For determination of top tier targets of each drug from KINOMEscan and Targetome V2 data, a threshold was used that was 10-fold higher than the second lowest Kd for each drug (termed Tierl hits). A set of high confidence drug/target relationships requiring either a Tierl with at most a Kd of 25 in KINOMEscan and/or Targetome was defined. Additionally, Targetome was required to have at least two references with at least two unique supporting assay values. This high confidence set was combined with a small number of additional interactions annotated manually. The IUPHAR family classifications for targets were utilized (Armstrong et al., Nucleic Acids Res 48, D1006-D1021 , 2020). Targets were assigned to the lowest level of the family hierarchy as well as up to two higher levels. The resulting inhibitor/family relationships were additionally manually curated.
[0209] Inhibitor family response. To generate gene family scores, the inhibitor AUC responses were first rescaled to be between 0 and 1 in order to ensure comparability between inhibitors with different concentration ranges. The single sample GSEA (ssGSEA; Barbie etal., Nature 462, I OS- 112, 2009) approach was then used as implemented in GSVA package (Hanzelmann etal., BMC bioinformatics 14, 7, 2013) to generate a score per family. At least 5 inhibitors per family were required and the cohort range normalization was not implemented as only a single patient was provided at a time. Note that the use of ssGSEA as opposed to a principal component-based scoring was driven by the need to account for differences in drug coverage amongst patients.
[0210] RNA Sequencing. Gene-level RNASeq counts were generated as in the previous Beat AML manuscript (Tyner et al., Nature 562, 526-531 , 2018). Again, conditional quantile normalization was used (CQN; Hansen etal., Biostatistics 13, 204-216, 2012). In order to facilitate comparison with previous results, the CQN reference distribution parameters learned from the Waves 1 +2 samples were used to apply the normalization to the Waves 3+4 samples.
Weighted Gene Co-expression Network Analysis (WGCNA)
[0211] Module Formation and Summarization. The original set of 14 gene expression modules (13 + ModO (grey) ‘outlier’ module) from the Beat AML manuscript derived using the WGCNA methodology (Zhang & Horvath, Stat Appl Genet Mol Biol 4, Article 17, 2005) was used. With the exception of ModO (grey), these gene sets are typically summarized by their ‘eigengene’ which is their first principal component (PC) score (Horvath & Dong, PLoS computational biology 4, e10001 17, 2008). As the modO (grey) gene-set was more heterogenous, the first 5 PC scores for the eigengene were kept. Since the CQN normalization approach described herein did not change the original data, the PC scores of the new cohort for each module were able to be directly ‘predicted’. First, the new cohort expression matrix was centered and scaled (C/S) using the mean and standard deviations estimated from the original cohort. Scores per new cohort patient were then formed as the linear combination of their C/S expression values and the corresponding column of the original matrix of eigenvectors/rotations.
[0212] Module Membership. A standard WGCNA methodology is the formation of kME values which are defined as the correlation of gene expression with the module eigengene (PC1 score as described herein) (Langfelder et al., Bioinformatics 24, 719-720, 2008). Genes with high kME are considered to have higher (fuzzy) membership in each module or alternatively can be seen as more ‘hub-like’ in a network context (Horvath and Dong, 2008). In this instance the correlation used was the robust biweight midcorrelation (Langfelder & Horvath, J Statistical Software 46, 2012).
[0213] Module Associations. To relate WGCNA modules to external continuous variables, direct correlation of a variable with a module eigengene was assessed, which indicates whether the pattern of a module covaries with the variable (Horvath et al., Proc Natl Acad Sci U S A 103, 17402-17407, 2006).
[0214] Cell-type Scores. The Waves 1 +2 cohort with RNASeq samples were first divided by whether they were bone marrow derived or from peripheral blood/leukapheresis and centered and scaled separately. Using the top 30 genes for each of six single-cell AML tumor-derived signatures (van Galen etal., Cell V? , 1265-1281 e1224, 2019), gene set scores were computed. Similar to the WGCNA eigengenes, the scores were based on the first principal component and aligned with the average expression. This is similar to the approach used in the context of pathway-based geneset analysis (PLAGE; Tomfohr et al., BMC bioinformatics 6, 225, 2005). Waves 3+4 cell-type scores were ‘predicted’ from ‘Waves 1 +2’ using the same approach described for the WGCNA eigengenes but this time using separate centering and scaling for the specimen types.
[0215] Formation of Genomic Features. The set of expression module and cell-type scores from the RNASeq samples were first combined. The two continuous datatypes were correlated, often with modules having opposing correlations with cell-types associated with low and high differentiation. To address this, the expression modules seen as being both positively and negatively correlated (abs ( r )>=.6) with a cell-type were removed. The consensus AML fusions and mutation calls were then added in as binary features.
[0216] Random Survival Forest. Using the combined set of genomic features, a conditional random forest (cforest) model was fit using the 'partykit' package with post-diagnosis survival, measured in days, as the outcome (Hothorn et al., J Computa Graph Stat 15, 651 -674, 2006). The model utilized 1 ,000 trees and 63.2% subsampling as opposed to the bootstrap as previously suggested (Strobl et al., BMC Bioinformatics 8, 25, 2007). The predictions (i.e. Iog10 median survival) from this model were used to fit a single surrogate conditional inference tree model to facilitate interpretation (Pearson’s correlation between model predictions: .877).
[0217] Where indicated, high vs low group categorizations were also performed using ctree but limiting depth to a single split and requiring minimum group size of 20.
[0218] LSC17. For comparisons with the LSC17 signature (Ng etal., Nature 540, 433-437, 2016), the signature was replicated using the coefficients in the manuscript, which was reported as the optimized signature by the authors (which was also described in WO2017132749A1 ). Categorization of expression for Hazard Ratio and other analyses was done using two different “split types”: median as described in (Ng et al., Nature 540, 433-437, 2016) or ctree to facilitate comparison with PEAR1.
[0219] Clinical curation via Natural Language Processing (NLP). An NLP workflow was developed to automatically extract key clinical data elements that were only available in the Electronic Health Records (EHR) in unstructured clinical notes. The NLP pipeline was written in Python which incorporated a commercially available text miner - Linguamatics’ Interactive Information Extraction (I2E) platform, as a major component, along with Python-based document preprocessing, results output logic, and evaluation components. Data from the manually curated Gold Standard Data Set (GSDS) from Waves 1 +2 were used to evaluate the performance of the NLP system. An NLP Data Set (NLPDS) was assembled that contained as far as possible the source pathology documents set from which the GSDS was originally obtained by manual review supervised by the data manager. This was subdivided into 5 partitions, and used in an iterative training-development-validation cycle to optimize the NLP and Python code. For evaluation purposes, partitions 1 and 2 were treated as a single training set and partitions 3, 4, and 5 as a single test set. Overall, the training set consisted of 108 patients with 134 specimens and 241 documents, and the test set consisted of 252 patients with 289 specimens and 532 documents. Evaluation of NLP results led in some cases to discovery of missing or incorrect data in the GSDS (which was estimated to have an overall error rate of 9%), further improving data quality. For the NLP data by data element, Accuracy ranged from 79% to 93%, Precision 85% to 96%, Recall from 76% to 93% and F1 -score (the harmonic mean of the precision and recall) 81 % to 94%.
Quantification and Statistical Analysis.
[0220] In all cases, statistical analyses of data, including tests used, exact value of n (where n indicates the number of patient specimens that were available for a given analysis), definition of center, dispersion, and precision measures, and correction for false discovery rate (FDR) were reported herein, including the Brief Description of the Drawings, the Examples, and the Figures. Measures of significance or correlation are reported as p- or R-values, with p-values corrected for FDR, where appropriate. All patient specimens with material sufficient for analysis and with data passing quality control thresholds (described herein and in Tyner et al., Nature 562, 526-531 , 2018) were included in the cohort and dataset.
Additional Resources.
[0221] All data can be accessed and queried through an online, interactive user interface,
Vizome, at vizome.org. [0222] Resources table
Figure imgf000056_0001
Figure imgf000057_0001
Figure imgf000058_0001
Figure imgf000059_0001
Figure imgf000060_0001
Figure imgf000061_0001
Figure imgf000062_0001
Data and Code Availability.
[0223] All raw and processed sequencing data, along with relevant clinical annotations have been submitted to dbGaP and Genomic Data Commons and are publicly available as of the date of publication. The dbGaP study ID is 30641 and accession ID is phs001657.v2.p1 . All data can be accessed and queried through an online, interactive user interface, Vizome, at vizome.org. Original code for replicating the paper analyses based on publicly available data has been deposited at github.com/biodev/beataml2_manuscript and will be publicly available as of the date of publication.
(X) Variants.
[0224] Variants of the sequences disclosed and referenced herein are also included. Guidance in determining which amino acid residues can be substituted, inserted, or deleted without abolishing biological activity can be found using computer programs well known in the art, such as DNASTAR™ (Madison, Wisconsin) software. Preferably, amino acid changes in the protein variants disclosed herein are conservative amino acid changes, i.e., substitutions of similarly charged or uncharged amino acids. A conservative amino acid change involves substitution of one of a family of amino acids which are related in their side chains.
[0225] Functional variants include one or more residue additions or substitutions that do not substantially impact the physiological effects of the protein. Functional fragments include one or more deletions or truncations that do not substantially impact the physiological effects of the protein. A lack of substantial impact can be confirmed by observing experimentally comparable results in an activation study or a binding study. Functional variants and functional fragments of intracellular domains (e.g., intracellular signaling domains) transmit activation or inhibition signals comparable to a wild-type reference when in the activated state of the current disclosure. Functional variants and functional fragments of binding domains bind their cognate antigen or ligand at a level comparable to a wild-type reference.
[0226] In a peptide or protein, suitable conservative substitutions of amino acids are known to those of skill in this art and generally can be made without altering a biological activity of a resulting molecule. Those of skill in this art recognize that, in general, single amino acid substitutions in non-essential regions of a polypeptide do not substantially alter biological activity (see, e.g., Watson et al., Molecular Biology of the Gene, 4th Edition, 1987, The Benjamin/Cummings Pub. Co., p. 224). Naturally occurring amino acids are generally divided into conservative substitution families as follows: Group 1 : Alanine (Ala), Glycine (Gly), Serine (Ser), and Threonine (Thr); Group 2: (acidic): Aspartic acid (Asp), and Glutamic acid (Glu); Group 3: (acidic; also classified as polar, negatively charged residues and their amides): Asparagine (Asn), Glutamine (Gin), Asp, and Glu; Group 4: Gin and Asn; Group 5: (basic; also classified as polar, positively charged residues): Arginine (Arg), Lysine (Lys), and Histidine (His); Group 6 (large aliphatic, nonpolar residues): Isoleucine (lie), Leucine (Leu), Methionine (Met), Valine (Vai) and Cysteine (Cys); Group 7 (uncharged polar): Tyrosine (Tyr), Gly, Asn, Gin, Cys, Ser, and Thr; Group 8 (large aromatic residues): Phenylalanine (Phe), Tryptophan (Trp), and Tyr; Group 9 (nonpolar): Proline (Pro), Ala, Vai, Leu, lie, Phe, Met, and Trp; Group 11 (aliphatic): Gly, Ala, Vai, Leu, and lie; Group 10 (small aliphatic, nonpolar or slightly polar residues): Ala, Ser, Thr, Pro, and Gly; and Group 12 (sulfur-containing): Met and Cys. Additional information can be found in Creighton (1984) Proteins, W.H. Freeman and Company.
[0227] In making such changes, the hydropathic index of amino acids may be considered. The importance of the hydropathic amino acid index in conferring interactive biologic function on a protein is generally understood in the art (Kyte and Doolittle, 1982, J. Mol. Biol. 157(1 ), 105-32). Each amino acid has been assigned a hydropathic index on the basis of its hydrophobicity and charge characteristics (Kyte and Doolittle, 1982). These values are: lie (+4.5); Vai (+4.2); Leu (+3.8); Phe (+2.8); Cys (+2.5); Met (+1 .9); Ala (+1 .8); Gly (-0.4); Thr (-0.7); Ser (-0.8); Trp (-0.9); Tyr (-1.3); Pro (-1.6); His (-3.2); Glutamate (-3.5); Gin (-3.5); aspartate (-3.5); Asn (-3.5); Lys (-3.9); and Arg (-4.5).
[0228] It is known in the art that certain amino acids may be substituted by other amino acids having a similar hydropathic index or score and still result in a protein with similar biological activity, i.e., still obtain a biological functionally equivalent protein. In making such changes, the substitution of amino acids whose hydropathic indices are within ±2 is preferred, those within ±1 are particularly preferred, and those within ±0.5 are even more particularly preferred. It is also understood in the art that the substitution of like amino acids can be made effectively on the basis of hydrophilicity.
[0229] As detailed in U.S. Pat. No. 4,554,101 , the following hydrophilicity values have been assigned to amino acid residues: Arg (+3.0); Lys (+3.0); aspartate (+3.0±1 ); glutamate (+3.0±1 ); Ser (+0.3); Asn (+0.2); Gin (+0.2); Gly (0); Thr (-0.4); Pro (-0.5±1); Ala (-0.5); His (-0.5); Cys (-1.0); Met (-1.3); Vai (-1.5); Leu (-1.8); lie (-1.8); Tyr (-2.3); Phe (-2.5); Trp (-3.4). It is understood that an amino acid can be substituted for another having a similar hydrophilicity value and still obtain a biologically equivalent, and in particular, an immunologically equivalent protein. In such changes, the substitution of amino acids whose hydrophilicity values are within ±2 is preferred, those within ±1 are particularly preferred, and those within ±0.5 are even more particularly preferred.
[0230] As outlined above, amino acid substitutions may be based on the relative similarity of the amino acid side-chain substituents, for example, their hydrophobicity, hydrophilicity, charge, size, and the like.
[0231] As indicated elsewhere, variants of gene sequences can include codon optimized variants, sequence polymorphisms, splice variants, and/or mutations that do not affect the function of an encoded product to a statistically-significant degree.
[0232] Variants of the protein, nucleic acid, and gene sequences disclosed herein also include sequences with at least 70% sequence identity, 80% sequence identity, 85% sequence, 90% sequence identity, 95% sequence identity, 96% sequence identity, 97% sequence identity, 98% sequence identity, or 99% sequence identity to the protein, nucleic acid, or gene sequences disclosed herein.
[0233] “% sequence identity” refers to a relationship between two or more sequences, as determined by comparing the sequences. In the art, "identity" also means the degree of sequence relatedness between protein, nucleic acid, or gene sequences as determined by the match between strings of such sequences. "Identity" (often referred to as "similarity") can be readily calculated by known methods, including (but not limited to) those described in: Computational Molecular Biology (Lesk, A. M., ed.) Oxford University Press, NY (1988); Biocomputing: Informatics and Genome Projects (Smith, D. W., ed.) Academic Press, NY (1994); Computer Analysis of Sequence Data, Part I (Griffin, A. M., and Griffin, H. G., eds.) Humana Press, NJ (1994); Sequence Analysis in Molecular Biology (Von Heijne, G., ed.) Academic Press (1987); and Sequence Analysis Primer (Gribskov, M. and Devereux, J., eds.) Oxford University Press, NY (1992). Preferred methods to determine identity are designed to give the best match between the sequences tested. Methods to determine identity and similarity are codified in publicly available computer programs. Sequence alignments and percent identity calculations may be performed using the Megalign program of the LASERGENE bioinformatics computing suite (DNASTAR, Inc., Madison, Wisconsin). Multiple alignment of the sequences can also be performed using the Clustal method of alignment (Higgins and Sharp CABIOS, 5, 151 -153 (1989) with default parameters (GAP PENALTY=10, GAP LENGTH PENALTY=10). Relevant programs also include the GCG suite of programs (Wisconsin Package Version 9.0, Genetics Computer Group (GCG), Madison, Wisconsin); BLASTP, BLASTN, BLASTX (Altschul, et aL, J. Mol. Biol. 215:403-410 (1990); DNASTAR (DNASTAR, Inc., Madison, Wisconsin); and the PASTA program incorporating the Smith-Waterman algorithm (Pearson, Comput. Methods Genome Res., [Proc. Int. Symp.] (1994), Meeting Date 1992, 11 1 -20. Editor(s): Suhai, Sandor. Publisher: Plenum, New York, N.Y.. Within the context of this disclosure it will be understood that where sequence analysis software is used for analysis, the results of the analysis are based on the "default values" of the program referenced. As used herein "default values" will mean any set of values or parameters, which originally load with the software when first initialized.
[0234] Variants also include nucleic acid molecules that hybridizes under stringent hybridization conditions to a sequence disclosed herein and provide the same function as the reference sequence. Exemplary stringent hybridization conditions include an overnight incubation at 42 °C in a solution including 50% formamide, 5XSSC (750 mM NaCI, 75 mM trisodium citrate), 50 mM sodium phosphate (pH 7.6), 5X Denhardt's solution, 10% dextran sulfate, and 20 pg/ml denatured, sheared salmon sperm DNA, followed by washing the filters in 0.1 XSSC at 50 °C. Changes in the stringency of hybridization and signal detection are primarily accomplished through the manipulation of formamide concentration (lower percentages of formamide result in lowered stringency); salt conditions, or temperature. For example, moderately high stringency conditions include an overnight incubation at 37°C in a solution including 6XSSPE (20XSSPE=3M NaCI; 0.2 M NaH2PO4; 0.02 M EDTA, pH 7.4), 0.5% SDS, 30% formamide, 100 pg/ml salmon sperm blocking DNA; followed by washes at 50 °C with 1 XSSPE, 0.1% SDS. In addition, to achieve even lower stringency, washes performed following stringent hybridization can be done at higher salt concentrations (e.g. 5XSSC). Variations in the above conditions may be accomplished through the inclusion and/or substitution of alternate blocking reagents used to suppress background in hybridization experiments. Typical blocking reagents include Denhardt's reagent, BLOTTO, heparin, denatured salmon sperm DNA, and commercially available proprietary formulations. The inclusion of specific blocking reagents may require modification of the hybridization conditions described above, due to problems with compatibility.
(XI) Closing Paragraphs.
[0235] Each embodiment disclosed herein can comprise, consist essentially of or consist of its particular stated element, step, ingredient or component. Thus, the terms “include” or “including” should be interpreted to recite: “comprise, consist of, or consist essentially of.” The transition term “comprise” or “comprises” means has, but is not limited to, and allows for the inclusion of unspecified elements, steps, ingredients, or components, even in major amounts. The transitional phrase “consisting of” excludes any element, step, ingredient or component not specified. The transition phrase “consisting essentially of” limits the scope of the embodiment to the specified elements, steps, ingredients or components and to those that do not materially affect the embodiment. A material effect would cause a statistically significant reduction in the ability to use PEAR1 as a prognostic biomarker for subjects < 45 years old having AML.
[0236] Unless otherwise indicated, all numbers expressing quantities of ingredients, properties such as molecular weight, reaction conditions, and so forth used in the specification and claims are to be understood as being modified in all instances by the term “about.” Accordingly, unless indicated to the contrary, the numerical parameters set forth in the specification and attached claims are approximations that may vary depending upon the desired properties sought to be obtained by the present invention. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of the claims, each numerical parameter should at least be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. When further clarity is required, the term “about” has the meaning reasonably ascribed to it by a person skilled in the art when used in conjunction with a stated numerical value or range, i.e. denoting somewhat more or somewhat less than the stated value or range, to within a range of ±20% of the stated value; ±19% of the stated value; ±18% of the stated value; ±17% of the stated value; ±16% of the stated value; ±15% of the stated value; ±14% of the stated value; ±13% of the stated value; ±12% of the stated value; ±11 % of the stated value; ±10% of the stated value; ±9% of the stated value; ±8% of the stated value; ±7% of the stated value; ±6% of the stated value; ±5% of the stated value; ±4% of the stated value; ±3% of the stated value; ±2% of the stated value; or ±1% of the stated value.
[0237] Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the invention are approximations, the numerical values set forth in the specific examples are reported as precisely as possible. Any numerical value, however, inherently contains certain errors necessarily resulting from the standard deviation found in their respective testing measurements.
[0238] The terms “a,” “an,” “the” and similar referents used in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. Recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.
[0239] Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member may be referred to and claimed individually or in any combination with other members of the group or other elements found herein. It is anticipated that one or more members of a group may be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.
[0240] Certain embodiments of this invention are described herein, including the best mode known to the inventors for carrying out the invention. Of course, variations on these described embodiments will become apparent to those of ordinary skill in the art upon reading the foregoing description. The inventor expects skilled artisans to employ such variations as appropriate, and the inventors intend for the invention to be practiced otherwise than specifically described herein. Accordingly, this invention includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Moreover, any combination of the above-described elements in all possible variations thereof is encompassed by the invention unless otherwise indicated herein or otherwise clearly contradicted by context.
[0241] Furthermore, numerous references have been made to patents, printed publications, journal articles and other written text throughout this specification (referenced materials herein). Each of the referenced materials are individually incorporated herein by reference in their entirety for their referenced teaching. [0242] It is to be understood that the embodiments of the invention disclosed herein are illustrative of the principles of the present invention. Other modifications that may be employed are within the scope of the invention. Thus, by way of example, but not of limitation, alternative configurations of the present invention may be utilized in accordance with the teachings herein. Accordingly, the present invention is not limited to that precisely as shown and described.
[0243] The particulars shown herein are by way of example and for purposes of illustrative discussion of the preferred embodiments of the present invention only and are presented in the cause of providing what is believed to be the most useful and readily understood description of the principles and conceptual aspects of various embodiments of the invention. In this regard, no attempt is made to show structural details of the invention in more detail than is necessary for the fundamental understanding of the invention, the description taken with the drawings and/or examples making apparent to those skilled in the art how the several forms of the invention may be embodied in practice.
[0244] Definitions and explanations used in the present disclosure are meant and intended to be controlling in any future construction unless clearly and unambiguously modified in the example(s) or when application of the meaning renders any construction meaningless or essentially meaningless. In cases where the construction of the term would render it meaningless or essentially meaningless, the definition should be taken from Webster's Dictionary, 11 th Edition or a dictionary known to those of ordinary skill in the art, such as the Oxford Dictionary of Biochemistry and Molecular Biology, 2nd Edition (Ed. Anthony Smith, Oxford University Press, Oxford, 2006).

Claims

LISTING OF CLAIMS We claim:
1 . A method for determining the prognosis for a subject < 45 years old having acute myeloid leukemia (AML), comprising: obtaining a biological sample derived from the subject; measuring in the biological sample a level of platelet endothelial aggregation receptor 1 (PEAR1); comparing the measured level of PEAR1 to a threshold level; and assigning to the subject a poor prognosis when the measured level of PEAR1 is greater than the threshold level.
2. The method of claim 1 , wherein the method does not comprise measuring in the biological sample a level of pyrin domain (PYD) and caspase activation and recruitment domain (CARD) containing (PYCARD) gene or protein.
3. The method of claim 1 , wherein the threshold level comprises 0.329 PEAR1 RNA reads per kilobase of transcript per million total reads (RPKM).
4. The method of claim 1 , wherein the biological sample comprises peripheral blood or bone marrow aspirate.
5. The method of claim 1 , wherein the subject has one or more mutations or gene rearrangements in TP53, RUNX1 , ASXL1 , SRSF2, or GATA2-MECOM.
6. The method of claim 1 , wherein the method further comprises performing a stem cell transplant for the subject.
7. A method of treating a subject < 45 years old having acute myeloid leukemia (AML), comprising administering a therapeutically effective amount of a drug that reduces or eliminates expression of PEAR1 or comprises an antagonist of PEAR1 function.
8. The method of claim 7, wherein the drug comprises a nucleic acid.
9. The method of claim 7, wherein the drug comprises an inhibitor of an integrin subunit.
10. The method of claim 7, wherein the drug comprises eptifibatide.
11 . The method of claim 7, wherein the drug comprises an antibody, or binding fragment thereof that binds to PEAR1 .
12. A method for identifying a subject having acute myeloid leukemia (AML) who is responsive to a drug, comprising: obtaining a biological sample derived from a subject having AML; determining a differentiation state of cells in the biological sample; determining mutational status of the subject; and identifying the subject as responsive to a drug when
(a) the differentiation state of cells is hematopoietic stem cell (HSC)-like and the subject has an MLLT3-KMT2A gene fusion;
(b) the differentiation state of cells is progenitor-like and the subject has an FLT3-ITD mutation;
(c) the differentiation state of cells is promonocyte-like and the subject has at least one mutation in KRAS;
(d) the differentiation state of cells is monocyte-like and the subject has at least one mutation in NRAS; and/or
(e) the differentiation state of cells is monocyte-like and the subject has at least one mutation in U2AF1 .
13. A method of diagnosing a subject as having drug responsive acute myeloid leukemia (AML), comprising: obtaining a biological sample derived from a subject having AML; determining a differentiation state of cells in the biological sample; determining mutational status of the subject; and identifying the subject as responsive to a drug when
(a) the differentiation state of cells is hematopoietic stem cell (HSC)-like and the subject has an MLLT3-KMT2A gene fusion;
(b) the differentiation state of cells is progenitor-like and the subject has an FLT3-ITD mutation; (c) the differentiation state of cells is promonocyte-like and the subject has at least one mutation in KRAS;
(d) the differentiation state of cells is monocyte-like and the subject has at least one mutation in NRAS; and/or
(e) the differentiation state of cells is monocyte-like and the subject has at least one mutation in U2AF1 .
14. The method of claim 12 or claim 13, wherein for (a) the drug comprises vismodegib (GDC-0449); for (b) the drug comprises sorafenib; for (c) the drug comprises CHIR-99021 ; for (d) the drug comprises entospletinib (GS-9973), lapatinib, sunitinib, or MGCD-265; and for (e) the drug comprises AGI-6780.
15. A method for identifying a subject having acute myeloid leukemia (AML) who is resistant to a drug, comprising: obtaining a biological sample derived from a subject having AML; determining a differentiation state of cells in the biological sample; determining mutational status of the subject; and identifying the subject as resistant to a drug when
(a) the differentiation state of cells is promonocyte-like and the subject has an FLT3- ITD mutation;
(b) the differentiation state of cells is monocyte-like and the subject has an FLT3-ITD mutation;
(c) the differentiation state of cells is monocyte-like and the subject has at least one mutation in IDH2; and/or
(d) the differentiation state of cells is monocyte-like and the subject has at least one mutation in SRSF2.
16. A method of diagnosing a subject as having drug resistant acute myeloid leukemia (AML), comprising: obtaining a biological sample derived from a subject having AML; determining a differentiation state of cells in the biological sample; determining mutational status of the subject; and identifying the subject as resistant to a drug when
(a) the differentiation state of cells is promonocyte-like and the subject has an FLT3- ITD mutation;
(b) the differentiation state of cells is monocyte-like and the subject has an FLT3-ITD mutation;
(c) the differentiation state of cells is monocyte-like and the subject has at least one mutation in IDH2; and/or
(d) the differentiation state of cells is monocyte-like and the subject has at least one mutation in SRSF2.
17. The method of claim 15 or claim 16, wherein for (a) the drug comprises cabozantinib, MGCD-265, or sunitinib; for (b) the drug comprises MGCD-265, sunitinib, AZD1152-HQPA (AZD2811 ), canertinib, sorafenib, cabozantinib, or NVP-ADW742; for (c) the drug comprises MLN8054; and for (d) the drug comprises CHIR-99021 .
18. A method for identifying a subject having acute myeloid leukemia (AML) who is responsive to a drug, comprising: obtaining a biological sample derived from a subject having AML; determining mutational status of the subject; and identifying that the subject is responsive to a drug when
(a) the subject has at least one mutation in NRAS, and wherein the drug comprises a cyclin-dependent kinase (CDK) inhibitor;
(b) the subject has at least one mutation in KRAS, and wherein the drug comprises a cyclin-dependent kinase (CDK) inhibitor;
(c) the subject has a CBFB-MYH11 gene fusion, and wherein the drug comprises a AGC kinase inhibitor;
(d) the subject has at least one mutation in RUNX1 , and wherein the drug comprises a phosphatidylinositol kinase inhibitor;
(e) the subject has at least one mutation in STAG2, and wherein the drug comprises a phosphatidylinositol kinase inhibitor;
(f) the subject has at least one mutation in TP53, and wherein the drug comprises a phosphatidylinositol kinase inhibitor; (g) the subject has at least one mutation in IDH2, and wherein the drug comprises a SRC kinase inhibitor;
(h) the subject has at least one mutation in ASXL1 , and wherein the drug comprises a JAK kinase inhibitor; and/or
(i) the subject has at least one mutation in TP53, and wherein the drug comprises a phosphatidylinositol-3 kinase-related kinase inhibitor.
19. A method for identifying a subject having acute myeloid leukemia (AML) who is responsive to a drug, comprising: obtaining a biological sample derived from a subject having AML; determining mutational status of the subject; and identifying that the subject is responsive to a drug when
(a) the subject has at least one mutation in NRAS, and wherein the drug comprises a cyclin-dependent kinase (CDK) inhibitor;
(b) the subject has at least one mutation in KRAS, and wherein the drug comprises a cyclin-dependent kinase (CDK) inhibitor;
(c) the subject has a CBFB-MYH11 gene fusion, and wherein the drug comprises a AGC kinase inhibitor;
(d) the subject has at least one mutation in RUNX1 , and wherein the drug comprises a phosphatidylinositol kinase inhibitor;
(e) the subject has at least one mutation in STAG2, and wherein the drug comprises a phosphatidylinositol kinase inhibitor;
(f) the subject has at least one mutation in TP53, and wherein the drug comprises a phosphatidylinositol kinase inhibitor;
(g) the subject has at least one mutation in IDH2, and wherein the drug comprises a SRC kinase inhibitor;
(h) the subject has at least one mutation in ASXL1 , and wherein the drug comprises a JAK kinase inhibitor; and/or
(i) the subject has at least one mutation in TP53, and wherein the drug comprises a phosphatidylinositol-3 kinase-related kinase inhibitor.
20. The method of claim 18 or claim 19, wherein for (a) and (b) the drug comprises JNJ-7706621 , R547, roscovitine (CYC-202), flavopiridol, palbociclib, AST-487, AT7519, BMS-345541 , linifanib (ABT-869), or SNS-032 (BMS- 387032); for (c) the drug comprises H-89, Go6976, LY-333531 , PP242, Midostaurin, BMS-345541 , AKT Inhibitor IV, GSK690693, MK-2206, or AKT Inhibitor X; for (d) to (f) the drug comprises PI-103, TG100-115, BEZ235, GDC-0941 , LY294002, Idelalisib, or PP242; for (g) the drug comprises bosutinib (SKI-606), dasatinib, ibrutinib (PCI-32765), PD173955, ponatinib (AP24534), PP2, vandetanib (ZD6474), saracatinib (AZD0530), PLX-4720, KW-2449; for (h) the drug comprises JAK Inhibitor I, JNJ-7706621 , ruxolitinib (INCB018424), tofacitinib (CP-690550), CYT387, PP242, TG101348, midostaurin, pelitinib (EKB-569), or BMS- 345541 ; and for (i) the drug comprises BEZ235, PI-103, PP242, Rapamycin, INK-128, or KU-55933.
21 . A method of modifying a treatment regimen of a subject < 45 years old having acute myeloid leukemia (AML), comprising: obtaining a biological sample derived from the subject; measuring in the biological sample a level of platelet endothelial aggregation receptor 1 (PEAR1); comparing the measured level of PEAR1 to a threshold level; and modifying the treatment regimen when the measured level of PEAR1 is greater than the threshold level.
22. The method of claim 21 , wherein the method does not comprise measuring in the biological sample a level of pyrin domain (PYD) and caspase activation and recruitment domain (CARD) containing (PYCARD) gene or protein.
23. The method of claim 21 , wherein the threshold level comprises 0.329 PEAR1 RNA reads per kilobase of transcript per million total reads (RPKM).
24. The method of claim 21 , wherein the biological sample comprises peripheral blood or bone marrow aspirate.
25. The method of claim 21 , wherein the modifying the treatment regimen comprises enrolling the subject in a clinical trial testing a drug for treatment of AML.
26. The method of claim 21 , wherein the modifying the treatment regimen comprises stopping chemotherapy and providing a stem cell transplant for the subject.
27. The method of claim 21 , wherein the modifying the treatment regimen comprises changing from one chemotherapy drug or a combination of chemotherapy drugs to another chemotherapy drug or another combination of chemotherapy drugs.
28. The method of claim 21 , wherein the subject has one or more mutations or gene rearrangements in TP53, RUNX1 , ASXL1 , SRSF2, or GATA2-MEC0M.
PCT/US2023/062078 2022-02-07 2023-02-06 Biomarkers for acute myeloid leukemia and uses thereof WO2023150768A2 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US202263307567P 2022-02-07 2022-02-07
US63/307,567 2022-02-07

Publications (2)

Publication Number Publication Date
WO2023150768A2 true WO2023150768A2 (en) 2023-08-10
WO2023150768A3 WO2023150768A3 (en) 2023-10-12

Family

ID=87553086

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2023/062078 WO2023150768A2 (en) 2022-02-07 2023-02-06 Biomarkers for acute myeloid leukemia and uses thereof

Country Status (1)

Country Link
WO (1) WO2023150768A2 (en)

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2547359B1 (en) * 2010-03-15 2016-03-09 The Board of Trustees of the University of Illionis Inhibitors of beta integrin-g protein alpha subunit binding interactions
US20140364439A1 (en) * 2011-12-07 2014-12-11 The Broad Institute, Inc. Markers associated with chronic lymphocytic leukemia prognosis and progression
GB201316187D0 (en) * 2013-09-11 2013-10-23 Genome Res Ltd Methods of modulating platelet aggregation, thrombus formation and stability, or an allergy response
HRP20220738T1 (en) * 2014-08-11 2022-08-19 Acerta Pharma B.V. Therapeutic combinations of a btk inhibitor, a pi3k inhibitor, a jak-2 inhibitor, a pd-1 inhibitor and/or a pd-l1 inhibitor
BR112020006106A2 (en) * 2017-09-28 2020-11-17 Immpact-Bio Ltd. methods to identify a target to prepare an inhibitory chimeric antigen receptor or a protective chimeric antigen receptor and to treat cancer in a patient having a tumor, and a safe effector immune cell

Also Published As

Publication number Publication date
WO2023150768A3 (en) 2023-10-12

Similar Documents

Publication Publication Date Title
Bernard et al. Molecular international prognostic scoring system for myelodysplastic syndromes
US20210317532A1 (en) Genetic alterations in isocitrate dehydrogenase and other genes in malignant glioma
Coston et al. Suboptimal response rates to hypomethylating agent therapy in chronic myelomonocytic leukemia; a single institutional study of 121 patients
Niktoreh et al. Mutated WT1, FLT3-ITD, and NUP98-NSD1 fusion in various combinations define a poor prognostic group in pediatric acute myeloid leukemia
JP2023169202A (en) System and method for identifying cancer treatment from normalized biomarker score
Aggarwal et al. Assessment of tumor mutational burden and outcomes in patients with diverse advanced cancers treated with immunotherapy
Yamato et al. ASXL2 mutations are frequently found in pediatric AML patients with t (8; 21)/RUNX1‐RUNX1T1 and associated with a better prognosis
Hofste op Bruinink et al. Identification of high-risk multiple myeloma with a plasma cell leukemia-like transcriptomic profile
Kampa-Schittenhelm et al. Epigenetic activation of O-linked β-N-acetylglucosamine transferase overrides the differentiation blockage in acute leukemia
Lee et al. Clinico‐genetic and prognostic analyses of 716 patients with primary myelodysplastic syndrome and myelodysplastic syndrome/acute myeloid leukemia based on the 2022 International Consensus Classification
Papageorgiou et al. The prognostic value of monosomal karyotype (MK) in higher‐risk patients with myelodysplastic syndromes treated with 5‐azacitidine: a retrospective analysis of the Hellenic (Greek) Myelodysplastic syndromes Study Group
WO2023150768A2 (en) Biomarkers for acute myeloid leukemia and uses thereof
Hsieh et al. Effect of allogeneic hematopoietic stem cell transplantation from matched siblings or unrelated donors during the first complete remission in patients with cytogenetically normal acute myeloid leukemia
Kratz et al. Incorporating genomics into the care of patients with advanced breast cancer
Smith et al. Detection of a GLIS3 fusion in an infant with AML refractory to chemotherapy
Yang et al. Prognosis and risk factors for ASXL1 mutations in patients with newly diagnosed acute myeloid leukemia and myelodysplastic syndrome
Zhao et al. The myelodysplastic syndromes: analysis of prognostic factors and comparison of prognostic systems in 128 Chinese patients from a single institution
Bramlett et al. RNA splicing factor Rbm25 underlies heterogeneous preleukemic clonal expansion in mice
Singh et al. Eosinophilia characterized by a rare CCT6B mutation and responsive to tyrosine kinase inhibition: case report and literature review
Nadiminti et al. OncoTargets and Therapy Dovepress
és de Valdecilla et al. Integrated flow cytometry and sequencing to reconstruct evolutionary patterns from dysplasia to acute myeloid leukemia
Ayala et al. The Mutational Landscape of Acute Myeloid Leukaemia Predicts Responses and Outcomes in Elderly Patients from the PETHEMA-FLUGAZA Phase 3 Clinical Trial. Cancers 2021, 13, 2458
AU2013201554B2 (en) Genetic alterations in isocitrate dehydrogenase and other genes in malignant glioma
Wu et al. Abnormal expression of CUX1 influences autophagy activation in paroxysmal nocturnal hemoglobinuria
Bayrak et al. Overview of clinical and genetic features of CML patients with variant Philadelphia translocations involving chromosome 7: A case series

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 23750494

Country of ref document: EP

Kind code of ref document: A2